{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lab_black extension is already loaded. To reload it, use:\n",
      "  %reload_ext lab_black\n"
     ]
    }
   ],
   "source": [
    "%load_ext lab_black\n",
    "\"\"\"Black Formatter for lab\"\"\"\n",
    "\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import IndexSlice as idx\n",
    "from pathlib import Path\n",
    "import quantstats as qs\n",
    "import sqlite3\n",
    "import yfinance as yf\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 200)\n",
    "pd.set_option(\"display.max_columns\", 80)\n",
    "# pd.set_option(\"display.precision\", 2)\n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batchname</th>\n",
       "      <th>from_date</th>\n",
       "      <th>trade_start</th>\n",
       "      <th>to_date</th>\n",
       "      <th>instrument</th>\n",
       "      <th>benchmark</th>\n",
       "      <th>commission</th>\n",
       "      <th>mult</th>\n",
       "      <th>sma_fast</th>\n",
       "      <th>sma_slow</th>\n",
       "      <th>limit_price</th>\n",
       "      <th>stop_price</th>\n",
       "      <th>trade_size</th>\n",
       "      <th>len</th>\n",
       "      <th>drawdown</th>\n",
       "      <th>moneydown</th>\n",
       "      <th>max_len</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>max_moneydown</th>\n",
       "      <th>total_total</th>\n",
       "      <th>total_open</th>\n",
       "      <th>total_closed</th>\n",
       "      <th>streak_won_current</th>\n",
       "      <th>streak_won_longest</th>\n",
       "      <th>streak_lost_current</th>\n",
       "      <th>streak_lost_longest</th>\n",
       "      <th>pnl_gross_total</th>\n",
       "      <th>pnl_gross_average</th>\n",
       "      <th>pnl_net_total</th>\n",
       "      <th>pnl_net_average</th>\n",
       "      <th>won_total</th>\n",
       "      <th>won_pnl_total</th>\n",
       "      <th>won_pnl_average</th>\n",
       "      <th>won_pnl_max</th>\n",
       "      <th>lost_total</th>\n",
       "      <th>lost_pnl_total</th>\n",
       "      <th>lost_pnl_average</th>\n",
       "      <th>lost_pnl_max</th>\n",
       "      <th>long_total</th>\n",
       "      <th>long_pnl_total</th>\n",
       "      <th>...</th>\n",
       "      <th>short_pnl_lost_average</th>\n",
       "      <th>short_pnl_lost_max</th>\n",
       "      <th>short_won</th>\n",
       "      <th>short_lost</th>\n",
       "      <th>len_total</th>\n",
       "      <th>len_average</th>\n",
       "      <th>len_max</th>\n",
       "      <th>len_min</th>\n",
       "      <th>len_won_total</th>\n",
       "      <th>len_won_average</th>\n",
       "      <th>len_won_max</th>\n",
       "      <th>len_won_min</th>\n",
       "      <th>len_lost_total</th>\n",
       "      <th>len_lost_average</th>\n",
       "      <th>len_lost_max</th>\n",
       "      <th>len_lost_min</th>\n",
       "      <th>len_long_total</th>\n",
       "      <th>len_long_average</th>\n",
       "      <th>len_long_max</th>\n",
       "      <th>len_long_min</th>\n",
       "      <th>len_long_won_total</th>\n",
       "      <th>len_long_won_average</th>\n",
       "      <th>len_long_won_max</th>\n",
       "      <th>len_long_won_min</th>\n",
       "      <th>len_long_lost_total</th>\n",
       "      <th>len_long_lost_average</th>\n",
       "      <th>len_long_lost_max</th>\n",
       "      <th>len_long_lost_min</th>\n",
       "      <th>len_short_total</th>\n",
       "      <th>len_short_average</th>\n",
       "      <th>len_short_max</th>\n",
       "      <th>len_short_min</th>\n",
       "      <th>len_short_won_total</th>\n",
       "      <th>len_short_won_average</th>\n",
       "      <th>len_short_won_max</th>\n",
       "      <th>len_short_won_min</th>\n",
       "      <th>len_short_lost_total</th>\n",
       "      <th>len_short_lost_average</th>\n",
       "      <th>len_short_lost_max</th>\n",
       "      <th>len_short_lost_min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0b8d79027f</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>7.4674</td>\n",
       "      <td>12259.8528</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>67899.5763</td>\n",
       "      <td>8487.4470</td>\n",
       "      <td>67899.5763</td>\n",
       "      <td>8487.4470</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>72904.0279</td>\n",
       "      <td>10414.8611</td>\n",
       "      <td>13619.1846</td>\n",
       "      <td>1.0000</td>\n",
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       "      <td>-5004.4516</td>\n",
       "      <td>-5004.4516</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>67899.5763</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>143.0000</td>\n",
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       "      <td>1.0000</td>\n",
       "      <td>142.0000</td>\n",
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       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>143.0000</td>\n",
       "      <td>17.8750</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>142.0000</td>\n",
       "      <td>20.2857</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4a221191bc</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0700</td>\n",
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       "      <td>1.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>4.2770</td>\n",
       "      <td>7121.4544</td>\n",
       "      <td>77.0000</td>\n",
       "      <td>5.9890</td>\n",
       "      <td>8690.5678</td>\n",
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       "      <td>5.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>59382.5745</td>\n",
       "      <td>5938.2574</td>\n",
       "      <td>59382.5745</td>\n",
       "      <td>5938.2574</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>71902.6909</td>\n",
       "      <td>8987.8364</td>\n",
       "      <td>14485.9825</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-12520.1164</td>\n",
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       "      <td>-7121.4544</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>59382.5745</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>149.0000</td>\n",
       "      <td>14.9000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>149.0000</td>\n",
       "      <td>14.9000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1.0000</td>\n",
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       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3e15e6fd83</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>7.3640</td>\n",
       "      <td>9863.8168</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>5.0000</td>\n",
       "      <td>57865.4998</td>\n",
       "      <td>11573.1000</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>5.0000</td>\n",
       "      <td>57865.4998</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>189.0000</td>\n",
       "      <td>37.8000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>189.0000</td>\n",
       "      <td>37.8000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>189.0000</td>\n",
       "      <td>37.8000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>189.0000</td>\n",
       "      <td>37.8000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>9223372036854775808.0000</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bc2ebdb49f</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>6.7066</td>\n",
       "      <td>7951.4886</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>56802.8590</td>\n",
       "      <td>11360.5718</td>\n",
       "      <td>56802.8590</td>\n",
       "      <td>11360.5718</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>56802.8590</td>\n",
       "      <td>11360.5718</td>\n",
       "      <td>15890.6493</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>28.8000</td>\n",
       "      <td>54.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>144.0000</td>\n",
       "      <td>28.8000</td>\n",
       "      <td>54.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>144.0000</td>\n",
       "      <td>28.8000</td>\n",
       "      <td>54.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>144.0000</td>\n",
       "      <td>28.8000</td>\n",
       "      <td>54.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d607db580f</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>4.2770</td>\n",
       "      <td>6973.2409</td>\n",
       "      <td>249.0000</td>\n",
       "      <td>11.7052</td>\n",
       "      <td>14465.0849</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>56065.4639</td>\n",
       "      <td>5606.5464</td>\n",
       "      <td>56065.4639</td>\n",
       "      <td>5606.5464</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>74636.5276</td>\n",
       "      <td>10662.3611</td>\n",
       "      <td>13669.2087</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>-18571.0637</td>\n",
       "      <td>-6190.3546</td>\n",
       "      <td>-6973.2409</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>56065.4639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>225.0000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>192.0000</td>\n",
       "      <td>27.4286</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>225.0000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>192.0000</td>\n",
       "      <td>27.4286</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61230bed74</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>3.6945</td>\n",
       "      <td>5954.8804</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>5.7162</td>\n",
       "      <td>6925.7280</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>55226.3595</td>\n",
       "      <td>6903.2949</td>\n",
       "      <td>55226.3595</td>\n",
       "      <td>6903.2949</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>60143.3523</td>\n",
       "      <td>8591.9075</td>\n",
       "      <td>13647.8726</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-4916.9928</td>\n",
       "      <td>-4916.9928</td>\n",
       "      <td>-4916.9928</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>55226.3595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>132.0000</td>\n",
       "      <td>18.8571</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>132.0000</td>\n",
       "      <td>18.8571</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>054298a8ee</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>7.4674</td>\n",
       "      <td>11256.2774</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>51028.1868</td>\n",
       "      <td>6378.5233</td>\n",
       "      <td>51028.1868</td>\n",
       "      <td>6378.5233</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>55949.1396</td>\n",
       "      <td>7992.7342</td>\n",
       "      <td>10554.5064</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-4920.9528</td>\n",
       "      <td>-4920.9528</td>\n",
       "      <td>-4920.9528</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>51028.1868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>113.0000</td>\n",
       "      <td>14.1250</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>112.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>113.0000</td>\n",
       "      <td>14.1250</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>112.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc34685a7c</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>6.3771</td>\n",
       "      <td>10278.7510</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>6.3771</td>\n",
       "      <td>10278.7510</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>50902.4890</td>\n",
       "      <td>6362.8111</td>\n",
       "      <td>50902.4890</td>\n",
       "      <td>6362.8111</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>60143.3523</td>\n",
       "      <td>8591.9075</td>\n",
       "      <td>13647.8726</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-9240.8633</td>\n",
       "      <td>-9240.8633</td>\n",
       "      <td>-9240.8633</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>50902.4890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>132.0000</td>\n",
       "      <td>18.8571</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>132.0000</td>\n",
       "      <td>18.8571</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dde6684d87</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>6.9770</td>\n",
       "      <td>11267.1373</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>8.8207</td>\n",
       "      <td>12799.7415</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>50221.5045</td>\n",
       "      <td>5022.1505</td>\n",
       "      <td>50221.5045</td>\n",
       "      <td>5022.1505</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>70996.4775</td>\n",
       "      <td>8874.5597</td>\n",
       "      <td>14485.9825</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-20774.9730</td>\n",
       "      <td>-10387.4865</td>\n",
       "      <td>-11267.1373</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>50221.5045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>152.0000</td>\n",
       "      <td>15.2000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>152.0000</td>\n",
       "      <td>15.2000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>134.0000</td>\n",
       "      <td>16.7500</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdb4b669f8</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>3.6945</td>\n",
       "      <td>5725.0417</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>4.6167</td>\n",
       "      <td>6658.4178</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>49235.1354</td>\n",
       "      <td>5470.5706</td>\n",
       "      <td>49235.1354</td>\n",
       "      <td>5470.5706</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>53962.3485</td>\n",
       "      <td>6745.2936</td>\n",
       "      <td>13527.7433</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-4727.2131</td>\n",
       "      <td>-4727.2131</td>\n",
       "      <td>-4727.2131</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>49235.1354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>7.6667</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>8.3750</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>7.6667</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>8.3750</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab2251d684</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>3.6945</td>\n",
       "      <td>5716.7739</td>\n",
       "      <td>201.0000</td>\n",
       "      <td>9.6027</td>\n",
       "      <td>14101.3663</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>49019.6171</td>\n",
       "      <td>6127.4521</td>\n",
       "      <td>49019.6171</td>\n",
       "      <td>6127.4521</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>61511.4813</td>\n",
       "      <td>10251.9136</td>\n",
       "      <td>14353.2891</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-12491.8642</td>\n",
       "      <td>-6245.9321</td>\n",
       "      <td>-7771.4779</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>49019.6171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>178.0000</td>\n",
       "      <td>22.2500</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>152.0000</td>\n",
       "      <td>25.3333</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>178.0000</td>\n",
       "      <td>22.2500</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>152.0000</td>\n",
       "      <td>25.3333</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e179e8dd3c</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>116.0000</td>\n",
       "      <td>7.4674</td>\n",
       "      <td>10715.7809</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>46753.3990</td>\n",
       "      <td>5844.1749</td>\n",
       "      <td>46753.3990</td>\n",
       "      <td>5844.1749</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>56192.0467</td>\n",
       "      <td>9365.3411</td>\n",
       "      <td>11903.9111</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-9438.6477</td>\n",
       "      <td>-4719.3239</td>\n",
       "      <td>-5004.4516</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>46753.3990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>138.0000</td>\n",
       "      <td>17.2500</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>138.0000</td>\n",
       "      <td>17.2500</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ed8704f6e0</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>6.6575</td>\n",
       "      <td>8151.4444</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>9104.6183</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>9104.6183</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>9104.6183</td>\n",
       "      <td>10669.0401</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767c49f116</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>6.6575</td>\n",
       "      <td>8151.4444</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>9104.6183</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>9104.6183</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>9104.6183</td>\n",
       "      <td>10669.0401</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>45523.0914</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>154.0000</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cf777c4b2c</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>6.3771</td>\n",
       "      <td>9882.0252</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>6.3771</td>\n",
       "      <td>9882.0252</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>45078.1520</td>\n",
       "      <td>5008.6836</td>\n",
       "      <td>45078.1520</td>\n",
       "      <td>5008.6836</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>53962.3485</td>\n",
       "      <td>6745.2936</td>\n",
       "      <td>13527.7433</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-8884.1965</td>\n",
       "      <td>-8884.1965</td>\n",
       "      <td>-8884.1965</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>45078.1520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>7.6667</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>8.3750</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>7.6667</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>8.3750</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c108e3aef7</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>6.9770</td>\n",
       "      <td>10717.1949</td>\n",
       "      <td>249.0000</td>\n",
       "      <td>14.2297</td>\n",
       "      <td>17584.8785</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42889.2812</td>\n",
       "      <td>4288.9281</td>\n",
       "      <td>42889.2812</td>\n",
       "      <td>4288.9281</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>71978.5052</td>\n",
       "      <td>10282.6436</td>\n",
       "      <td>13278.3756</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>-29089.2240</td>\n",
       "      <td>-9696.4080</td>\n",
       "      <td>-10717.1949</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>42889.2812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>254.0000</td>\n",
       "      <td>25.4000</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>192.0000</td>\n",
       "      <td>27.4286</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>20.6667</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>254.0000</td>\n",
       "      <td>25.4000</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>192.0000</td>\n",
       "      <td>27.4286</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>20.6667</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b56d311260</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>215.0000</td>\n",
       "      <td>8.2897</td>\n",
       "      <td>8904.4585</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42820.0895</td>\n",
       "      <td>6117.1556</td>\n",
       "      <td>42820.0895</td>\n",
       "      <td>6117.1556</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>47973.0250</td>\n",
       "      <td>7995.5042</td>\n",
       "      <td>11961.9002</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-5152.9354</td>\n",
       "      <td>-5152.9354</td>\n",
       "      <td>-5152.9354</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>42820.0895</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>118.0000</td>\n",
       "      <td>16.8571</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>19.1667</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>118.0000</td>\n",
       "      <td>16.8571</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>19.1667</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4b13190966</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>6.3771</td>\n",
       "      <td>9585.7269</td>\n",
       "      <td>201.0000</td>\n",
       "      <td>12.1863</td>\n",
       "      <td>17895.3678</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>40728.1937</td>\n",
       "      <td>5091.0242</td>\n",
       "      <td>40728.1937</td>\n",
       "      <td>5091.0242</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>60911.4898</td>\n",
       "      <td>10151.9150</td>\n",
       "      <td>14353.2891</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-20183.2961</td>\n",
       "      <td>-10091.6481</td>\n",
       "      <td>-11565.4793</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>40728.1937</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>192.0000</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>152.0000</td>\n",
       "      <td>25.3333</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>192.0000</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>152.0000</td>\n",
       "      <td>25.3333</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c6661bdcb3</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>219.0000</td>\n",
       "      <td>10.8923</td>\n",
       "      <td>11700.0343</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>38767.1238</td>\n",
       "      <td>5538.1605</td>\n",
       "      <td>38767.1238</td>\n",
       "      <td>5538.1605</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>46784.9947</td>\n",
       "      <td>7797.4991</td>\n",
       "      <td>11622.4440</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-8017.8709</td>\n",
       "      <td>-8017.8709</td>\n",
       "      <td>-8017.8709</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>38767.1238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>119.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>19.1667</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>119.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>19.1667</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f4c3ad3bf1</th>\n",
       "      <td>Single Test</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>FB</td>\n",
       "      <td>SPY</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>4.2770</td>\n",
       "      <td>6198.5829</td>\n",
       "      <td>89.0000</td>\n",
       "      <td>5.9890</td>\n",
       "      <td>8116.3165</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>38728.1370</td>\n",
       "      <td>3872.8137</td>\n",
       "      <td>38728.1370</td>\n",
       "      <td>3872.8137</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>49968.6516</td>\n",
       "      <td>6246.0815</td>\n",
       "      <td>13528.7845</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-11240.5146</td>\n",
       "      <td>-5620.2573</td>\n",
       "      <td>-6198.5829</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>38728.1370</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>93.0000</td>\n",
       "      <td>9.3000</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>78.0000</td>\n",
       "      <td>9.7500</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>93.0000</td>\n",
       "      <td>9.3000</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>78.0000</td>\n",
       "      <td>9.7500</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9223372036854775808.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               batchname   from_date trade_start     to_date instrument  \\\n",
       "test_number                                                               \n",
       "0b8d79027f   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "4a221191bc   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "3e15e6fd83   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "bc2ebdb49f   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "d607db580f   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "61230bed74   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "054298a8ee   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "fc34685a7c   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "dde6684d87   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "bdb4b669f8   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "ab2251d684   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "e179e8dd3c   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "ed8704f6e0   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "767c49f116   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "cf777c4b2c   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "c108e3aef7   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "b56d311260   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "4b13190966   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "c6661bdcb3   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "f4c3ad3bf1   Single Test  2016-01-01  2016-09-01  2020-12-31         FB   \n",
       "\n",
       "            benchmark  commission  mult  sma_fast  sma_slow  limit_price  \\\n",
       "test_number                                                                \n",
       "0b8d79027f        SPY      0.0000     1        15        60       0.0900   \n",
       "4a221191bc        SPY      0.0000     1        30        45       0.0700   \n",
       "3e15e6fd83        SPY      0.0000     1        15       120       0.0900   \n",
       "bc2ebdb49f        SPY      0.0000     1        30        75       0.0900   \n",
       "d607db580f        SPY      0.0000     1        30        45       0.0900   \n",
       "61230bed74        SPY      0.0000     1        15        45       0.0700   \n",
       "054298a8ee        SPY      0.0000     1        15        60       0.0700   \n",
       "fc34685a7c        SPY      0.0000     1        15        45       0.0700   \n",
       "dde6684d87        SPY      0.0000     1        30        45       0.0700   \n",
       "bdb4b669f8        SPY      0.0000     1        15        45       0.0500   \n",
       "ab2251d684        SPY      0.0000     1        15        45       0.0900   \n",
       "e179e8dd3c        SPY      0.0000     1        15        60       0.0900   \n",
       "ed8704f6e0        SPY      0.0000     1        30        90       0.0900   \n",
       "767c49f116        SPY      0.0000     1        30        90       0.0900   \n",
       "cf777c4b2c        SPY      0.0000     1        15        45       0.0500   \n",
       "c108e3aef7        SPY      0.0000     1        30        45       0.0900   \n",
       "b56d311260        SPY      0.0000     1        15        75       0.0700   \n",
       "4b13190966        SPY      0.0000     1        15        45       0.0900   \n",
       "c6661bdcb3        SPY      0.0000     1        15        75       0.0700   \n",
       "f4c3ad3bf1        SPY      0.0000     1        30        45       0.0500   \n",
       "\n",
       "             stop_price  trade_size     len  drawdown  moneydown  max_len  \\\n",
       "test_number                                                                 \n",
       "0b8d79027f       0.0800      1.0000  0.0000    0.0000     0.0000  52.0000   \n",
       "4a221191bc       0.0500      1.0000 37.0000    4.2770  7121.4544  77.0000   \n",
       "3e15e6fd83       0.0800      1.0000  0.0000    0.0000     0.0000  19.0000   \n",
       "bc2ebdb49f       0.0800      1.0000  0.0000    0.0000     0.0000  18.0000   \n",
       "d607db580f       0.0500      1.0000 37.0000    4.2770  6973.2409 249.0000   \n",
       "61230bed74       0.0500      1.0000 42.0000    3.6945  5954.8804  42.0000   \n",
       "054298a8ee       0.0800      1.0000  0.0000    0.0000     0.0000  52.0000   \n",
       "fc34685a7c       0.0800      1.0000 42.0000    6.3771 10278.7510  42.0000   \n",
       "dde6684d87       0.0800      1.0000 37.0000    6.9770 11267.1373  88.0000   \n",
       "bdb4b669f8       0.0500      1.0000 42.0000    3.6945  5725.0417  42.0000   \n",
       "ab2251d684       0.0500      1.0000 42.0000    3.6945  5716.7739 201.0000   \n",
       "e179e8dd3c       0.0500      1.0000  0.0000    0.0000     0.0000 116.0000   \n",
       "ed8704f6e0       0.0500      1.0000  0.0000    0.0000     0.0000  14.0000   \n",
       "767c49f116       0.0800      1.0000  0.0000    0.0000     0.0000  14.0000   \n",
       "cf777c4b2c       0.0800      1.0000 42.0000    6.3771  9882.0252  42.0000   \n",
       "c108e3aef7       0.0800      1.0000 37.0000    6.9770 10717.1949 249.0000   \n",
       "b56d311260       0.0500      1.0000  0.0000    0.0000     0.0000 215.0000   \n",
       "4b13190966       0.0800      1.0000 42.0000    6.3771  9585.7269 201.0000   \n",
       "c6661bdcb3       0.0800      1.0000  0.0000    0.0000     0.0000 219.0000   \n",
       "f4c3ad3bf1       0.0500      1.0000 37.0000    4.2770  6198.5829  89.0000   \n",
       "\n",
       "             max_drawdown  max_moneydown  total_total  total_open  \\\n",
       "test_number                                                         \n",
       "0b8d79027f         7.4674     12259.8528       8.0000      0.0000   \n",
       "4a221191bc         5.9890      8690.5678      10.0000      0.0000   \n",
       "3e15e6fd83         7.3640      9863.8168       5.0000      0.0000   \n",
       "bc2ebdb49f         6.7066      7951.4886       5.0000      0.0000   \n",
       "d607db580f        11.7052     14465.0849      10.0000      0.0000   \n",
       "61230bed74         5.7162      6925.7280       8.0000      0.0000   \n",
       "054298a8ee         7.4674     11256.2774       8.0000      0.0000   \n",
       "fc34685a7c         6.3771     10278.7510       8.0000      0.0000   \n",
       "dde6684d87         8.8207     12799.7415      10.0000      0.0000   \n",
       "bdb4b669f8         4.6167      6658.4178       9.0000      0.0000   \n",
       "ab2251d684         9.6027     14101.3663       8.0000      0.0000   \n",
       "e179e8dd3c         7.4674     10715.7809       8.0000      0.0000   \n",
       "ed8704f6e0         6.6575      8151.4444       5.0000      0.0000   \n",
       "767c49f116         6.6575      8151.4444       5.0000      0.0000   \n",
       "cf777c4b2c         6.3771      9882.0252       9.0000      0.0000   \n",
       "c108e3aef7        14.2297     17584.8785      10.0000      0.0000   \n",
       "b56d311260         8.2897      8904.4585       7.0000      0.0000   \n",
       "4b13190966        12.1863     17895.3678       8.0000      0.0000   \n",
       "c6661bdcb3        10.8923     11700.0343       7.0000      0.0000   \n",
       "f4c3ad3bf1         5.9890      8116.3165      10.0000      0.0000   \n",
       "\n",
       "             total_closed  streak_won_current  streak_won_longest  \\\n",
       "test_number                                                         \n",
       "0b8d79027f         8.0000              6.0000              6.0000   \n",
       "4a221191bc        10.0000              0.0000              5.0000   \n",
       "3e15e6fd83         5.0000              5.0000              5.0000   \n",
       "bc2ebdb49f         5.0000              5.0000              5.0000   \n",
       "d607db580f        10.0000              0.0000              3.0000   \n",
       "61230bed74         8.0000              0.0000              7.0000   \n",
       "054298a8ee         8.0000              6.0000              6.0000   \n",
       "fc34685a7c         8.0000              0.0000              7.0000   \n",
       "dde6684d87        10.0000              0.0000              5.0000   \n",
       "bdb4b669f8         9.0000              0.0000              8.0000   \n",
       "ab2251d684         8.0000              0.0000              4.0000   \n",
       "e179e8dd3c         8.0000              4.0000              4.0000   \n",
       "ed8704f6e0         5.0000              5.0000              5.0000   \n",
       "767c49f116         5.0000              5.0000              5.0000   \n",
       "cf777c4b2c         9.0000              0.0000              8.0000   \n",
       "c108e3aef7        10.0000              0.0000              3.0000   \n",
       "b56d311260         7.0000              5.0000              5.0000   \n",
       "4b13190966         8.0000              0.0000              4.0000   \n",
       "c6661bdcb3         7.0000              5.0000              5.0000   \n",
       "f4c3ad3bf1        10.0000              0.0000              5.0000   \n",
       "\n",
       "             streak_lost_current  streak_lost_longest  pnl_gross_total  \\\n",
       "test_number                                                              \n",
       "0b8d79027f                0.0000               1.0000       67899.5763   \n",
       "4a221191bc                1.0000               1.0000       59382.5745   \n",
       "3e15e6fd83                0.0000               0.0000       57865.4998   \n",
       "bc2ebdb49f                0.0000               0.0000       56802.8590   \n",
       "d607db580f                1.0000               1.0000       56065.4639   \n",
       "61230bed74                1.0000               1.0000       55226.3595   \n",
       "054298a8ee                0.0000               1.0000       51028.1868   \n",
       "fc34685a7c                1.0000               1.0000       50902.4890   \n",
       "dde6684d87                1.0000               1.0000       50221.5045   \n",
       "bdb4b669f8                1.0000               1.0000       49235.1354   \n",
       "ab2251d684                1.0000               1.0000       49019.6171   \n",
       "e179e8dd3c                0.0000               1.0000       46753.3990   \n",
       "ed8704f6e0                0.0000               0.0000       45523.0914   \n",
       "767c49f116                0.0000               0.0000       45523.0914   \n",
       "cf777c4b2c                1.0000               1.0000       45078.1520   \n",
       "c108e3aef7                1.0000               1.0000       42889.2812   \n",
       "b56d311260                0.0000               1.0000       42820.0895   \n",
       "4b13190966                1.0000               1.0000       40728.1937   \n",
       "c6661bdcb3                0.0000               1.0000       38767.1238   \n",
       "f4c3ad3bf1                1.0000               1.0000       38728.1370   \n",
       "\n",
       "             pnl_gross_average  pnl_net_total  pnl_net_average  won_total  \\\n",
       "test_number                                                                 \n",
       "0b8d79027f           8487.4470     67899.5763        8487.4470     7.0000   \n",
       "4a221191bc           5938.2574     59382.5745        5938.2574     8.0000   \n",
       "3e15e6fd83          11573.1000     57865.4998       11573.1000     5.0000   \n",
       "bc2ebdb49f          11360.5718     56802.8590       11360.5718     5.0000   \n",
       "d607db580f           5606.5464     56065.4639        5606.5464     7.0000   \n",
       "61230bed74           6903.2949     55226.3595        6903.2949     7.0000   \n",
       "054298a8ee           6378.5233     51028.1868        6378.5233     7.0000   \n",
       "fc34685a7c           6362.8111     50902.4890        6362.8111     7.0000   \n",
       "dde6684d87           5022.1505     50221.5045        5022.1505     8.0000   \n",
       "bdb4b669f8           5470.5706     49235.1354        5470.5706     8.0000   \n",
       "ab2251d684           6127.4521     49019.6171        6127.4521     6.0000   \n",
       "e179e8dd3c           5844.1749     46753.3990        5844.1749     6.0000   \n",
       "ed8704f6e0           9104.6183     45523.0914        9104.6183     5.0000   \n",
       "767c49f116           9104.6183     45523.0914        9104.6183     5.0000   \n",
       "cf777c4b2c           5008.6836     45078.1520        5008.6836     8.0000   \n",
       "c108e3aef7           4288.9281     42889.2812        4288.9281     7.0000   \n",
       "b56d311260           6117.1556     42820.0895        6117.1556     6.0000   \n",
       "4b13190966           5091.0242     40728.1937        5091.0242     6.0000   \n",
       "c6661bdcb3           5538.1605     38767.1238        5538.1605     6.0000   \n",
       "f4c3ad3bf1           3872.8137     38728.1370        3872.8137     8.0000   \n",
       "\n",
       "             won_pnl_total  won_pnl_average  won_pnl_max  lost_total  \\\n",
       "test_number                                                            \n",
       "0b8d79027f      72904.0279       10414.8611   13619.1846      1.0000   \n",
       "4a221191bc      71902.6909        8987.8364   14485.9825      2.0000   \n",
       "3e15e6fd83      57865.4998       11573.1000   15834.6385      0.0000   \n",
       "bc2ebdb49f      56802.8590       11360.5718   15890.6493      0.0000   \n",
       "d607db580f      74636.5276       10662.3611   13669.2087      3.0000   \n",
       "61230bed74      60143.3523        8591.9075   13647.8726      1.0000   \n",
       "054298a8ee      55949.1396        7992.7342   10554.5064      1.0000   \n",
       "fc34685a7c      60143.3523        8591.9075   13647.8726      1.0000   \n",
       "dde6684d87      70996.4775        8874.5597   14485.9825      2.0000   \n",
       "bdb4b669f8      53962.3485        6745.2936   13527.7433      1.0000   \n",
       "ab2251d684      61511.4813       10251.9136   14353.2891      2.0000   \n",
       "e179e8dd3c      56192.0467        9365.3411   11903.9111      2.0000   \n",
       "ed8704f6e0      45523.0914        9104.6183   10669.0401      0.0000   \n",
       "767c49f116      45523.0914        9104.6183   10669.0401      0.0000   \n",
       "cf777c4b2c      53962.3485        6745.2936   13527.7433      1.0000   \n",
       "c108e3aef7      71978.5052       10282.6436   13278.3756      3.0000   \n",
       "b56d311260      47973.0250        7995.5042   11961.9002      1.0000   \n",
       "4b13190966      60911.4898       10151.9150   14353.2891      2.0000   \n",
       "c6661bdcb3      46784.9947        7797.4991   11622.4440      1.0000   \n",
       "f4c3ad3bf1      49968.6516        6246.0815   13528.7845      2.0000   \n",
       "\n",
       "             lost_pnl_total  lost_pnl_average  lost_pnl_max  long_total  \\\n",
       "test_number                                                               \n",
       "0b8d79027f       -5004.4516        -5004.4516    -5004.4516      8.0000   \n",
       "4a221191bc      -12520.1164        -6260.0582    -7121.4544     10.0000   \n",
       "3e15e6fd83           0.0000            0.0000        0.0000      5.0000   \n",
       "bc2ebdb49f           0.0000            0.0000        0.0000      5.0000   \n",
       "d607db580f      -18571.0637        -6190.3546    -6973.2409     10.0000   \n",
       "61230bed74       -4916.9928        -4916.9928    -4916.9928      8.0000   \n",
       "054298a8ee       -4920.9528        -4920.9528    -4920.9528      8.0000   \n",
       "fc34685a7c       -9240.8633        -9240.8633    -9240.8633      8.0000   \n",
       "dde6684d87      -20774.9730       -10387.4865   -11267.1373     10.0000   \n",
       "bdb4b669f8       -4727.2131        -4727.2131    -4727.2131      9.0000   \n",
       "ab2251d684      -12491.8642        -6245.9321    -7771.4779      8.0000   \n",
       "e179e8dd3c       -9438.6477        -4719.3239    -5004.4516      8.0000   \n",
       "ed8704f6e0           0.0000            0.0000        0.0000      5.0000   \n",
       "767c49f116           0.0000            0.0000        0.0000      5.0000   \n",
       "cf777c4b2c       -8884.1965        -8884.1965    -8884.1965      9.0000   \n",
       "c108e3aef7      -29089.2240        -9696.4080   -10717.1949     10.0000   \n",
       "b56d311260       -5152.9354        -5152.9354    -5152.9354      7.0000   \n",
       "4b13190966      -20183.2961       -10091.6481   -11565.4793      8.0000   \n",
       "c6661bdcb3       -8017.8709        -8017.8709    -8017.8709      7.0000   \n",
       "f4c3ad3bf1      -11240.5146        -5620.2573    -6198.5829     10.0000   \n",
       "\n",
       "             long_pnl_total  ...  short_pnl_lost_average  short_pnl_lost_max  \\\n",
       "test_number                  ...                                               \n",
       "0b8d79027f       67899.5763  ...                  0.0000              0.0000   \n",
       "4a221191bc       59382.5745  ...                  0.0000              0.0000   \n",
       "3e15e6fd83       57865.4998  ...                  0.0000              0.0000   \n",
       "bc2ebdb49f       56802.8590  ...                  0.0000              0.0000   \n",
       "d607db580f       56065.4639  ...                  0.0000              0.0000   \n",
       "61230bed74       55226.3595  ...                  0.0000              0.0000   \n",
       "054298a8ee       51028.1868  ...                  0.0000              0.0000   \n",
       "fc34685a7c       50902.4890  ...                  0.0000              0.0000   \n",
       "dde6684d87       50221.5045  ...                  0.0000              0.0000   \n",
       "bdb4b669f8       49235.1354  ...                  0.0000              0.0000   \n",
       "ab2251d684       49019.6171  ...                  0.0000              0.0000   \n",
       "e179e8dd3c       46753.3990  ...                  0.0000              0.0000   \n",
       "ed8704f6e0       45523.0914  ...                  0.0000              0.0000   \n",
       "767c49f116       45523.0914  ...                  0.0000              0.0000   \n",
       "cf777c4b2c       45078.1520  ...                  0.0000              0.0000   \n",
       "c108e3aef7       42889.2812  ...                  0.0000              0.0000   \n",
       "b56d311260       42820.0895  ...                  0.0000              0.0000   \n",
       "4b13190966       40728.1937  ...                  0.0000              0.0000   \n",
       "c6661bdcb3       38767.1238  ...                  0.0000              0.0000   \n",
       "f4c3ad3bf1       38728.1370  ...                  0.0000              0.0000   \n",
       "\n",
       "             short_won  short_lost  len_total  len_average  len_max  len_min  \\\n",
       "test_number                                                                    \n",
       "0b8d79027f      0.0000      0.0000   143.0000      17.8750  37.0000   1.0000   \n",
       "4a221191bc      0.0000      0.0000   149.0000      14.9000  30.0000   1.0000   \n",
       "3e15e6fd83      0.0000      0.0000   189.0000      37.8000  65.0000   8.0000   \n",
       "bc2ebdb49f      0.0000      0.0000   144.0000      28.8000  54.0000   3.0000   \n",
       "d607db580f      0.0000      0.0000   225.0000      22.5000  66.0000   1.0000   \n",
       "61230bed74      0.0000      0.0000   134.0000      16.7500  55.0000   2.0000   \n",
       "054298a8ee      0.0000      0.0000   113.0000      14.1250  26.0000   1.0000   \n",
       "fc34685a7c      0.0000      0.0000   134.0000      16.7500  55.0000   2.0000   \n",
       "dde6684d87      0.0000      0.0000   152.0000      15.2000  30.0000   2.0000   \n",
       "bdb4b669f8      0.0000      0.0000    69.0000       7.6667  28.0000   2.0000   \n",
       "ab2251d684      0.0000      0.0000   178.0000      22.2500  67.0000   2.0000   \n",
       "e179e8dd3c      0.0000      0.0000   138.0000      17.2500  37.0000   1.0000   \n",
       "ed8704f6e0      0.0000      0.0000   154.0000      30.8000  55.0000   1.0000   \n",
       "767c49f116      0.0000      0.0000   154.0000      30.8000  55.0000   1.0000   \n",
       "cf777c4b2c      0.0000      0.0000    69.0000       7.6667  28.0000   2.0000   \n",
       "c108e3aef7      0.0000      0.0000   254.0000      25.4000  66.0000   2.0000   \n",
       "b56d311260      0.0000      0.0000   118.0000      16.8571  33.0000   3.0000   \n",
       "4b13190966      0.0000      0.0000   192.0000      24.0000  67.0000   2.0000   \n",
       "c6661bdcb3      0.0000      0.0000   119.0000      17.0000  33.0000   4.0000   \n",
       "f4c3ad3bf1      0.0000      0.0000    93.0000       9.3000  21.0000   1.0000   \n",
       "\n",
       "             len_won_total  len_won_average  len_won_max  len_won_min  \\\n",
       "test_number                                                             \n",
       "0b8d79027f        142.0000          20.2857      37.0000       7.0000   \n",
       "4a221191bc        134.0000          16.7500      30.0000       9.0000   \n",
       "3e15e6fd83        189.0000          37.8000      65.0000       8.0000   \n",
       "bc2ebdb49f        144.0000          28.8000      54.0000       3.0000   \n",
       "d607db580f        192.0000          27.4286      66.0000       9.0000   \n",
       "61230bed74        132.0000          18.8571      55.0000       3.0000   \n",
       "054298a8ee        112.0000          16.0000      26.0000       7.0000   \n",
       "fc34685a7c        132.0000          18.8571      55.0000       3.0000   \n",
       "dde6684d87        134.0000          16.7500      30.0000       9.0000   \n",
       "bdb4b669f8         67.0000           8.3750      28.0000       3.0000   \n",
       "ab2251d684        152.0000          25.3333      67.0000       3.0000   \n",
       "e179e8dd3c        135.0000          22.5000      37.0000      10.0000   \n",
       "ed8704f6e0        154.0000          30.8000      55.0000       1.0000   \n",
       "767c49f116        154.0000          30.8000      55.0000       1.0000   \n",
       "cf777c4b2c         67.0000           8.3750      28.0000       3.0000   \n",
       "c108e3aef7        192.0000          27.4286      66.0000       9.0000   \n",
       "b56d311260        115.0000          19.1667      33.0000       4.0000   \n",
       "4b13190966        152.0000          25.3333      67.0000       3.0000   \n",
       "c6661bdcb3        115.0000          19.1667      33.0000       4.0000   \n",
       "f4c3ad3bf1         78.0000           9.7500      21.0000       2.0000   \n",
       "\n",
       "             len_lost_total  len_lost_average  len_lost_max  len_lost_min  \\\n",
       "test_number                                                                 \n",
       "0b8d79027f           1.0000            1.0000        1.0000        1.0000   \n",
       "4a221191bc          15.0000            7.5000       14.0000        1.0000   \n",
       "3e15e6fd83           0.0000            0.0000        0.0000        0.0000   \n",
       "bc2ebdb49f           0.0000            0.0000        0.0000        0.0000   \n",
       "d607db580f          33.0000           11.0000       18.0000        1.0000   \n",
       "61230bed74           2.0000            2.0000        2.0000        2.0000   \n",
       "054298a8ee           1.0000            1.0000        1.0000        1.0000   \n",
       "fc34685a7c           2.0000            2.0000        2.0000        2.0000   \n",
       "dde6684d87          18.0000            9.0000       16.0000        2.0000   \n",
       "bdb4b669f8           2.0000            2.0000        2.0000        2.0000   \n",
       "ab2251d684          26.0000           13.0000       24.0000        2.0000   \n",
       "e179e8dd3c           3.0000            1.5000        2.0000        1.0000   \n",
       "ed8704f6e0           0.0000            0.0000        0.0000        0.0000   \n",
       "767c49f116           0.0000            0.0000        0.0000        0.0000   \n",
       "cf777c4b2c           2.0000            2.0000        2.0000        2.0000   \n",
       "c108e3aef7          62.0000           20.6667       44.0000        2.0000   \n",
       "b56d311260           3.0000            3.0000        3.0000        3.0000   \n",
       "4b13190966          40.0000           20.0000       38.0000        2.0000   \n",
       "c6661bdcb3           4.0000            4.0000        4.0000        4.0000   \n",
       "f4c3ad3bf1          15.0000            7.5000       14.0000        1.0000   \n",
       "\n",
       "             len_long_total  len_long_average  len_long_max  len_long_min  \\\n",
       "test_number                                                                 \n",
       "0b8d79027f         143.0000           17.8750       37.0000        1.0000   \n",
       "4a221191bc         149.0000           14.9000       30.0000        1.0000   \n",
       "3e15e6fd83         189.0000           37.8000       65.0000        8.0000   \n",
       "bc2ebdb49f         144.0000           28.8000       54.0000        3.0000   \n",
       "d607db580f         225.0000           22.5000       66.0000        1.0000   \n",
       "61230bed74         134.0000           16.7500       55.0000        2.0000   \n",
       "054298a8ee         113.0000           14.1250       26.0000        1.0000   \n",
       "fc34685a7c         134.0000           16.7500       55.0000        2.0000   \n",
       "dde6684d87         152.0000           15.2000       30.0000        2.0000   \n",
       "bdb4b669f8          69.0000            7.6667       28.0000        2.0000   \n",
       "ab2251d684         178.0000           22.2500       67.0000        2.0000   \n",
       "e179e8dd3c         138.0000           17.2500       37.0000        1.0000   \n",
       "ed8704f6e0         154.0000           30.8000       55.0000        1.0000   \n",
       "767c49f116         154.0000           30.8000       55.0000        1.0000   \n",
       "cf777c4b2c          69.0000            7.6667       28.0000        2.0000   \n",
       "c108e3aef7         254.0000           25.4000       66.0000        2.0000   \n",
       "b56d311260         118.0000           16.8571       33.0000        3.0000   \n",
       "4b13190966         192.0000           24.0000       67.0000        2.0000   \n",
       "c6661bdcb3         119.0000           17.0000       33.0000        4.0000   \n",
       "f4c3ad3bf1          93.0000            9.3000       21.0000        1.0000   \n",
       "\n",
       "             len_long_won_total  len_long_won_average  len_long_won_max  \\\n",
       "test_number                                                               \n",
       "0b8d79027f             142.0000               20.2857           37.0000   \n",
       "4a221191bc             134.0000               16.7500           30.0000   \n",
       "3e15e6fd83             189.0000               37.8000           65.0000   \n",
       "bc2ebdb49f             144.0000               28.8000           54.0000   \n",
       "d607db580f             192.0000               27.4286           66.0000   \n",
       "61230bed74             132.0000               18.8571           55.0000   \n",
       "054298a8ee             112.0000               16.0000           26.0000   \n",
       "fc34685a7c             132.0000               18.8571           55.0000   \n",
       "dde6684d87             134.0000               16.7500           30.0000   \n",
       "bdb4b669f8              67.0000                8.3750           28.0000   \n",
       "ab2251d684             152.0000               25.3333           67.0000   \n",
       "e179e8dd3c             135.0000               22.5000           37.0000   \n",
       "ed8704f6e0             154.0000               30.8000           55.0000   \n",
       "767c49f116             154.0000               30.8000           55.0000   \n",
       "cf777c4b2c              67.0000                8.3750           28.0000   \n",
       "c108e3aef7             192.0000               27.4286           66.0000   \n",
       "b56d311260             115.0000               19.1667           33.0000   \n",
       "4b13190966             152.0000               25.3333           67.0000   \n",
       "c6661bdcb3             115.0000               19.1667           33.0000   \n",
       "f4c3ad3bf1              78.0000                9.7500           21.0000   \n",
       "\n",
       "             len_long_won_min  len_long_lost_total  len_long_lost_average  \\\n",
       "test_number                                                                 \n",
       "0b8d79027f             7.0000               1.0000                 1.0000   \n",
       "4a221191bc             9.0000              15.0000                 7.5000   \n",
       "3e15e6fd83             8.0000               0.0000                 0.0000   \n",
       "bc2ebdb49f             3.0000               0.0000                 0.0000   \n",
       "d607db580f             9.0000              33.0000                11.0000   \n",
       "61230bed74             3.0000               2.0000                 2.0000   \n",
       "054298a8ee             7.0000               1.0000                 1.0000   \n",
       "fc34685a7c             3.0000               2.0000                 2.0000   \n",
       "dde6684d87             9.0000              18.0000                 9.0000   \n",
       "bdb4b669f8             3.0000               2.0000                 2.0000   \n",
       "ab2251d684             3.0000              26.0000                13.0000   \n",
       "e179e8dd3c            10.0000               3.0000                 1.5000   \n",
       "ed8704f6e0             1.0000               0.0000                 0.0000   \n",
       "767c49f116             1.0000               0.0000                 0.0000   \n",
       "cf777c4b2c             3.0000               2.0000                 2.0000   \n",
       "c108e3aef7             9.0000              62.0000                20.6667   \n",
       "b56d311260             4.0000               3.0000                 3.0000   \n",
       "4b13190966             3.0000              40.0000                20.0000   \n",
       "c6661bdcb3             4.0000               4.0000                 4.0000   \n",
       "f4c3ad3bf1             2.0000              15.0000                 7.5000   \n",
       "\n",
       "             len_long_lost_max        len_long_lost_min  len_short_total  \\\n",
       "test_number                                                                \n",
       "0b8d79027f              1.0000                   1.0000           0.0000   \n",
       "4a221191bc             14.0000                   1.0000           0.0000   \n",
       "3e15e6fd83              0.0000 9223372036854775808.0000           0.0000   \n",
       "bc2ebdb49f              0.0000 9223372036854775808.0000           0.0000   \n",
       "d607db580f             18.0000                   1.0000           0.0000   \n",
       "61230bed74              2.0000                   2.0000           0.0000   \n",
       "054298a8ee              1.0000                   1.0000           0.0000   \n",
       "fc34685a7c              2.0000                   2.0000           0.0000   \n",
       "dde6684d87             16.0000                   2.0000           0.0000   \n",
       "bdb4b669f8              2.0000                   2.0000           0.0000   \n",
       "ab2251d684             24.0000                   2.0000           0.0000   \n",
       "e179e8dd3c              2.0000                   1.0000           0.0000   \n",
       "ed8704f6e0              0.0000 9223372036854775808.0000           0.0000   \n",
       "767c49f116              0.0000 9223372036854775808.0000           0.0000   \n",
       "cf777c4b2c              2.0000                   2.0000           0.0000   \n",
       "c108e3aef7             44.0000                   2.0000           0.0000   \n",
       "b56d311260              3.0000                   3.0000           0.0000   \n",
       "4b13190966             38.0000                   2.0000           0.0000   \n",
       "c6661bdcb3              4.0000                   4.0000           0.0000   \n",
       "f4c3ad3bf1             14.0000                   1.0000           0.0000   \n",
       "\n",
       "             len_short_average  len_short_max            len_short_min  \\\n",
       "test_number                                                              \n",
       "0b8d79027f              0.0000         0.0000 9223372036854775808.0000   \n",
       "4a221191bc              0.0000         0.0000 9223372036854775808.0000   \n",
       "3e15e6fd83              0.0000         0.0000 9223372036854775808.0000   \n",
       "bc2ebdb49f              0.0000         0.0000 9223372036854775808.0000   \n",
       "d607db580f              0.0000         0.0000 9223372036854775808.0000   \n",
       "61230bed74              0.0000         0.0000 9223372036854775808.0000   \n",
       "054298a8ee              0.0000         0.0000 9223372036854775808.0000   \n",
       "fc34685a7c              0.0000         0.0000 9223372036854775808.0000   \n",
       "dde6684d87              0.0000         0.0000 9223372036854775808.0000   \n",
       "bdb4b669f8              0.0000         0.0000 9223372036854775808.0000   \n",
       "ab2251d684              0.0000         0.0000 9223372036854775808.0000   \n",
       "e179e8dd3c              0.0000         0.0000 9223372036854775808.0000   \n",
       "ed8704f6e0              0.0000         0.0000 9223372036854775808.0000   \n",
       "767c49f116              0.0000         0.0000 9223372036854775808.0000   \n",
       "cf777c4b2c              0.0000         0.0000 9223372036854775808.0000   \n",
       "c108e3aef7              0.0000         0.0000 9223372036854775808.0000   \n",
       "b56d311260              0.0000         0.0000 9223372036854775808.0000   \n",
       "4b13190966              0.0000         0.0000 9223372036854775808.0000   \n",
       "c6661bdcb3              0.0000         0.0000 9223372036854775808.0000   \n",
       "f4c3ad3bf1              0.0000         0.0000 9223372036854775808.0000   \n",
       "\n",
       "             len_short_won_total  len_short_won_average  len_short_won_max  \\\n",
       "test_number                                                                  \n",
       "0b8d79027f                0.0000                 0.0000             0.0000   \n",
       "4a221191bc                0.0000                 0.0000             0.0000   \n",
       "3e15e6fd83                0.0000                 0.0000             0.0000   \n",
       "bc2ebdb49f                0.0000                 0.0000             0.0000   \n",
       "d607db580f                0.0000                 0.0000             0.0000   \n",
       "61230bed74                0.0000                 0.0000             0.0000   \n",
       "054298a8ee                0.0000                 0.0000             0.0000   \n",
       "fc34685a7c                0.0000                 0.0000             0.0000   \n",
       "dde6684d87                0.0000                 0.0000             0.0000   \n",
       "bdb4b669f8                0.0000                 0.0000             0.0000   \n",
       "ab2251d684                0.0000                 0.0000             0.0000   \n",
       "e179e8dd3c                0.0000                 0.0000             0.0000   \n",
       "ed8704f6e0                0.0000                 0.0000             0.0000   \n",
       "767c49f116                0.0000                 0.0000             0.0000   \n",
       "cf777c4b2c                0.0000                 0.0000             0.0000   \n",
       "c108e3aef7                0.0000                 0.0000             0.0000   \n",
       "b56d311260                0.0000                 0.0000             0.0000   \n",
       "4b13190966                0.0000                 0.0000             0.0000   \n",
       "c6661bdcb3                0.0000                 0.0000             0.0000   \n",
       "f4c3ad3bf1                0.0000                 0.0000             0.0000   \n",
       "\n",
       "                   len_short_won_min  len_short_lost_total  \\\n",
       "test_number                                                  \n",
       "0b8d79027f  9223372036854775808.0000                0.0000   \n",
       "4a221191bc  9223372036854775808.0000                0.0000   \n",
       "3e15e6fd83  9223372036854775808.0000                0.0000   \n",
       "bc2ebdb49f  9223372036854775808.0000                0.0000   \n",
       "d607db580f  9223372036854775808.0000                0.0000   \n",
       "61230bed74  9223372036854775808.0000                0.0000   \n",
       "054298a8ee  9223372036854775808.0000                0.0000   \n",
       "fc34685a7c  9223372036854775808.0000                0.0000   \n",
       "dde6684d87  9223372036854775808.0000                0.0000   \n",
       "bdb4b669f8  9223372036854775808.0000                0.0000   \n",
       "ab2251d684  9223372036854775808.0000                0.0000   \n",
       "e179e8dd3c  9223372036854775808.0000                0.0000   \n",
       "ed8704f6e0  9223372036854775808.0000                0.0000   \n",
       "767c49f116  9223372036854775808.0000                0.0000   \n",
       "cf777c4b2c  9223372036854775808.0000                0.0000   \n",
       "c108e3aef7  9223372036854775808.0000                0.0000   \n",
       "b56d311260  9223372036854775808.0000                0.0000   \n",
       "4b13190966  9223372036854775808.0000                0.0000   \n",
       "c6661bdcb3  9223372036854775808.0000                0.0000   \n",
       "f4c3ad3bf1  9223372036854775808.0000                0.0000   \n",
       "\n",
       "             len_short_lost_average  len_short_lost_max  \\\n",
       "test_number                                               \n",
       "0b8d79027f                   0.0000              0.0000   \n",
       "4a221191bc                   0.0000              0.0000   \n",
       "3e15e6fd83                   0.0000              0.0000   \n",
       "bc2ebdb49f                   0.0000              0.0000   \n",
       "d607db580f                   0.0000              0.0000   \n",
       "61230bed74                   0.0000              0.0000   \n",
       "054298a8ee                   0.0000              0.0000   \n",
       "fc34685a7c                   0.0000              0.0000   \n",
       "dde6684d87                   0.0000              0.0000   \n",
       "bdb4b669f8                   0.0000              0.0000   \n",
       "ab2251d684                   0.0000              0.0000   \n",
       "e179e8dd3c                   0.0000              0.0000   \n",
       "ed8704f6e0                   0.0000              0.0000   \n",
       "767c49f116                   0.0000              0.0000   \n",
       "cf777c4b2c                   0.0000              0.0000   \n",
       "c108e3aef7                   0.0000              0.0000   \n",
       "b56d311260                   0.0000              0.0000   \n",
       "4b13190966                   0.0000              0.0000   \n",
       "c6661bdcb3                   0.0000              0.0000   \n",
       "f4c3ad3bf1                   0.0000              0.0000   \n",
       "\n",
       "                  len_short_lost_min  \n",
       "test_number                           \n",
       "0b8d79027f  9223372036854775808.0000  \n",
       "4a221191bc  9223372036854775808.0000  \n",
       "3e15e6fd83  9223372036854775808.0000  \n",
       "bc2ebdb49f  9223372036854775808.0000  \n",
       "d607db580f  9223372036854775808.0000  \n",
       "61230bed74  9223372036854775808.0000  \n",
       "054298a8ee  9223372036854775808.0000  \n",
       "fc34685a7c  9223372036854775808.0000  \n",
       "dde6684d87  9223372036854775808.0000  \n",
       "bdb4b669f8  9223372036854775808.0000  \n",
       "ab2251d684  9223372036854775808.0000  \n",
       "e179e8dd3c  9223372036854775808.0000  \n",
       "ed8704f6e0  9223372036854775808.0000  \n",
       "767c49f116  9223372036854775808.0000  \n",
       "cf777c4b2c  9223372036854775808.0000  \n",
       "c108e3aef7  9223372036854775808.0000  \n",
       "b56d311260  9223372036854775808.0000  \n",
       "4b13190966  9223372036854775808.0000  \n",
       "c6661bdcb3  9223372036854775808.0000  \n",
       "f4c3ad3bf1  9223372036854775808.0000  \n",
       "\n",
       "[20 rows x 96 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_table(table_name, test_number=None):\n",
    "    \"\"\"\n",
    "    Collects a table for an individual test.\n",
    "    \"\"\"\n",
    "    # Creates connection and downloads the latest database data.\n",
    "    engine = create_db_connection()\n",
    "\n",
    "    if test_number:\n",
    "        sql_v = f\"SELECT * FROM {table_name} WHERE test_number='{test_number}';\"\n",
    "    else:\n",
    "        sql_v = f\"SELECT * FROM {table_name};\"\n",
    "\n",
    "    df = pd.read_sql(sql_v, con=engine)\n",
    "\n",
    "    try:\n",
    "        df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "        df[\"Date\"] = df[\"Date\"].dt.date\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    try:\n",
    "        del df[\"index\"]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def create_db_connection():\n",
    "    \"\"\"\n",
    "    Opens a database connection.\n",
    "    \"\"\"\n",
    "    dir = Path(\"data\")\n",
    "    filename = \"results.db\"\n",
    "    filepath = dir / filename\n",
    "    return sqlite3.connect(filepath)\n",
    "\n",
    "\n",
    "engine = create_db_connection()\n",
    "# Get all the table names.\n",
    "sql = \"SELECT name FROM sqlite_master WHERE type IN ('table','view') AND name NOT LIKE 'sqlite_%' ORDER BY 1;\"\n",
    "tables = pd.read_sql(sql, con=engine)[\"name\"].tolist()\n",
    "tables.remove(\"transaction\")\n",
    "\n",
    "single_res_tables = [\"dimension\", \"drawdown\", \"trade_analysis\"]\n",
    "df_combined = pd.DataFrame()\n",
    "for srt in single_res_tables:\n",
    "    if srt in tables:\n",
    "        df = get_table(srt)\n",
    "        df = df.set_index(\"test_number\")\n",
    "\n",
    "        if df_combined.empty:\n",
    "            df_combined = df\n",
    "        else:\n",
    "            df_combined = df_combined.join(df)\n",
    "df_combined.sort_values(\"pnl_gross_total\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_combined.to_excel(\"multi_test.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['benchmark', 'dimension', 'drawdown', 'global_out', 'ohlcv', 'order_history', 'quantstats', 'trade', 'trade_analysis', 'trade_list', 'value']\n"
     ]
    }
   ],
   "source": [
    "print(tables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new object for the pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_number</th>\n",
       "      <th>ref</th>\n",
       "      <th>ticker</th>\n",
       "      <th>dir</th>\n",
       "      <th>datein</th>\n",
       "      <th>pricein</th>\n",
       "      <th>dateout</th>\n",
       "      <th>priceout</th>\n",
       "      <th>chng_pct</th>\n",
       "      <th>pnl</th>\n",
       "      <th>pnl_pct</th>\n",
       "      <th>size</th>\n",
       "      <th>value</th>\n",
       "      <th>cumpnl</th>\n",
       "      <th>nbars</th>\n",
       "      <th>pnl/bar</th>\n",
       "      <th>mfe_pct</th>\n",
       "      <th>mae_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>18</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>128.2300</td>\n",
       "      <td>2017-03-10</td>\n",
       "      <td>139.4328</td>\n",
       "      <td>8.7400</td>\n",
       "      <td>7881.8949</td>\n",
       "      <td>7.3100</td>\n",
       "      <td>703.5647</td>\n",
       "      <td>90218.1051</td>\n",
       "      <td>7881.8949</td>\n",
       "      <td>35</td>\n",
       "      <td>225.2000</td>\n",
       "      <td>8.7800</td>\n",
       "      <td>-1.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>19</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>177.0100</td>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>167.4700</td>\n",
       "      <td>-5.3900</td>\n",
       "      <td>-5004.4516</td>\n",
       "      <td>-4.8600</td>\n",
       "      <td>524.5756</td>\n",
       "      <td>92855.1343</td>\n",
       "      <td>2877.4433</td>\n",
       "      <td>1</td>\n",
       "      <td>-5004.4500</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>-8.5100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>20</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>183.1500</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>199.0994</td>\n",
       "      <td>8.7100</td>\n",
       "      <td>8084.6937</td>\n",
       "      <td>7.2900</td>\n",
       "      <td>506.8964</td>\n",
       "      <td>92838.0782</td>\n",
       "      <td>10962.1370</td>\n",
       "      <td>26</td>\n",
       "      <td>310.9500</td>\n",
       "      <td>8.9700</td>\n",
       "      <td>-0.5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>21</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>149.2000</td>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>165.6000</td>\n",
       "      <td>10.9900</td>\n",
       "      <td>10915.7634</td>\n",
       "      <td>8.9600</td>\n",
       "      <td>665.5953</td>\n",
       "      <td>99306.8232</td>\n",
       "      <td>21877.9004</td>\n",
       "      <td>7</td>\n",
       "      <td>1559.3900</td>\n",
       "      <td>15.0700</td>\n",
       "      <td>-4.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>22</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>189.8800</td>\n",
       "      <td>2019-07-12</td>\n",
       "      <td>204.5494</td>\n",
       "      <td>7.7300</td>\n",
       "      <td>8574.4863</td>\n",
       "      <td>6.5700</td>\n",
       "      <td>584.5151</td>\n",
       "      <td>110987.7340</td>\n",
       "      <td>30452.3867</td>\n",
       "      <td>10</td>\n",
       "      <td>857.4500</td>\n",
       "      <td>8.1200</td>\n",
       "      <td>-0.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>23</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>187.2000</td>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>204.8001</td>\n",
       "      <td>9.4000</td>\n",
       "      <td>10997.8048</td>\n",
       "      <td>7.7800</td>\n",
       "      <td>624.8717</td>\n",
       "      <td>116975.9866</td>\n",
       "      <td>41450.1915</td>\n",
       "      <td>37</td>\n",
       "      <td>297.2400</td>\n",
       "      <td>10.2000</td>\n",
       "      <td>-1.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>24</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>201.6000</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>10.8600</td>\n",
       "      <td>13619.1846</td>\n",
       "      <td>8.7800</td>\n",
       "      <td>621.8806</td>\n",
       "      <td>125371.1238</td>\n",
       "      <td>55069.3761</td>\n",
       "      <td>13</td>\n",
       "      <td>1047.6300</td>\n",
       "      <td>14.7500</td>\n",
       "      <td>-1.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0b8d79027f</td>\n",
       "      <td>25</td>\n",
       "      <td>FB</td>\n",
       "      <td>long</td>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>267.3800</td>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>291.9000</td>\n",
       "      <td>9.1700</td>\n",
       "      <td>12830.2002</td>\n",
       "      <td>7.6400</td>\n",
       "      <td>523.2545</td>\n",
       "      <td>139907.7865</td>\n",
       "      <td>67899.5763</td>\n",
       "      <td>14</td>\n",
       "      <td>916.4400</td>\n",
       "      <td>11.2200</td>\n",
       "      <td>-3.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  test_number  ref ticker   dir      datein  pricein     dateout  priceout  \\\n",
       "0  0b8d79027f   18     FB  long  2017-01-19 128.2300  2017-03-10  139.4328   \n",
       "1  0b8d79027f   19     FB  long  2018-03-19 177.0100  2018-03-20  167.4700   \n",
       "2  0b8d79027f   20     FB  long  2018-05-10 183.1500  2018-06-18  199.0994   \n",
       "3  0b8d79027f   21     FB  long  2019-01-22 149.2000  2019-01-31  165.6000   \n",
       "4  0b8d79027f   22     FB  long  2019-06-27 189.8800  2019-07-12  204.5494   \n",
       "5  0b8d79027f   23     FB  long  2019-10-28 187.2000  2019-12-19  204.8001   \n",
       "6  0b8d79027f   24     FB  long  2020-05-01 201.6000  2020-05-20  223.5000   \n",
       "7  0b8d79027f   25     FB  long  2020-10-16 267.3800  2020-11-05  291.9000   \n",
       "\n",
       "   chng_pct        pnl  pnl_pct     size       value     cumpnl  nbars  \\\n",
       "0    8.7400  7881.8949   7.3100 703.5647  90218.1051  7881.8949     35   \n",
       "1   -5.3900 -5004.4516  -4.8600 524.5756  92855.1343  2877.4433      1   \n",
       "2    8.7100  8084.6937   7.2900 506.8964  92838.0782 10962.1370     26   \n",
       "3   10.9900 10915.7634   8.9600 665.5953  99306.8232 21877.9004      7   \n",
       "4    7.7300  8574.4863   6.5700 584.5151 110987.7340 30452.3867     10   \n",
       "5    9.4000 10997.8048   7.7800 624.8717 116975.9866 41450.1915     37   \n",
       "6   10.8600 13619.1846   8.7800 621.8806 125371.1238 55069.3761     13   \n",
       "7    9.1700 12830.2002   7.6400 523.2545 139907.7865 67899.5763     14   \n",
       "\n",
       "     pnl/bar  mfe_pct  mae_pct  \n",
       "0   225.2000   8.7800  -1.1300  \n",
       "1 -5004.4500   0.0900  -8.5100  \n",
       "2   310.9500   8.9700  -0.5300  \n",
       "3  1559.3900  15.0700  -4.4800  \n",
       "4   857.4500   8.1200  -0.8500  \n",
       "5   297.2400  10.2000  -1.1300  \n",
       "6  1047.6300  14.7500  -1.4100  \n",
       "7   916.4400  11.2200  -3.7500  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Enter a test number from above.\"\"\"\n",
    "\n",
    "tn = \"0b8d79027f\"\n",
    "\n",
    "df = get_table(\"trade_list\", tn).sort_values(\"ref\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>bm</th>\n",
       "      <th>value_return</th>\n",
       "      <th>bm_return</th>\n",
       "      <th>value_log</th>\n",
       "      <th>bm_log</th>\n",
       "      <th>value_rebase</th>\n",
       "      <th>bm_rebase</th>\n",
       "      <th>value_estdev</th>\n",
       "      <th>bm_estdev</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>value_drawdown</th>\n",
       "      <th>bm_drawdown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>2012.6600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>2016.7100</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.2012</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1990.2600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0131</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0132</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>98.8870</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1943.0900</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0237</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0240</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.5434</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1922.0300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0108</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0109</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.4970</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1923.6700</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.5785</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1938.6801</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.3243</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1890.2800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0253</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>93.9195</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1921.8400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.4876</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0167</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1880.3300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0216</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0218</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>93.4251</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1881.3300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>93.4748</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1859.3300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0117</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0118</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.3817</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1868.9900</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.8617</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0052</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1906.9000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.7453</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0203</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1877.0800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0156</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0158</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>93.2636</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1903.6300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.5828</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0141</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1882.9500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0109</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0109</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>93.5553</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1893.3600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.0725</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1940.2400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0248</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.4018</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0248</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1939.3800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.3590</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1903.0300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0187</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.5530</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1912.5300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.0250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1915.4500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.1701</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1880.0500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0185</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0187</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>93.4112</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1853.4399</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0142</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0143</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.0891</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-09</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1852.2100</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.0280</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-10</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1851.8600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.0106</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-11</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1829.0800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0123</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0124</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>90.8787</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-12</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1864.7800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0195</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.6525</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0195</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-16</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1895.5800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.1828</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1926.8199</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.7350</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-18</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1917.8300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0047</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0047</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.2883</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-19</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1917.7800</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.2858</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-22</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1945.5000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.6631</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>-0.0145</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-23</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1921.2700</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0125</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0125</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.4592</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-24</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1929.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.8831</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>-0.0044</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-25</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1951.7000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.9712</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>-0.0113</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-26</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1948.0500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.7898</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1932.2300</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0081</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0082</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>96.0038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>100000.0000</td>\n",
       "      <td>1978.3500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>98.2953</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>-0.0239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 value        bm  value_return  bm_return  value_log  bm_log  \\\n",
       "Date                                                                           \n",
       "2016-01-04 100000.0000 2012.6600        0.0000     0.0000     0.0000  0.0000   \n",
       "2016-01-05 100000.0000 2016.7100        0.0000     0.0020     0.0000  0.0020   \n",
       "2016-01-06 100000.0000 1990.2600        0.0000    -0.0131     0.0000 -0.0132   \n",
       "2016-01-07 100000.0000 1943.0900        0.0000    -0.0237     0.0000 -0.0240   \n",
       "2016-01-08 100000.0000 1922.0300        0.0000    -0.0108     0.0000 -0.0109   \n",
       "2016-01-11 100000.0000 1923.6700        0.0000     0.0009     0.0000  0.0009   \n",
       "2016-01-12 100000.0000 1938.6801        0.0000     0.0078     0.0000  0.0078   \n",
       "2016-01-13 100000.0000 1890.2800        0.0000    -0.0250     0.0000 -0.0253   \n",
       "2016-01-14 100000.0000 1921.8400        0.0000     0.0167     0.0000  0.0166   \n",
       "2016-01-15 100000.0000 1880.3300        0.0000    -0.0216     0.0000 -0.0218   \n",
       "2016-01-19 100000.0000 1881.3300        0.0000     0.0005     0.0000  0.0005   \n",
       "2016-01-20 100000.0000 1859.3300        0.0000    -0.0117     0.0000 -0.0118   \n",
       "2016-01-21 100000.0000 1868.9900        0.0000     0.0052     0.0000  0.0052   \n",
       "2016-01-22 100000.0000 1906.9000        0.0000     0.0203     0.0000  0.0201   \n",
       "2016-01-25 100000.0000 1877.0800        0.0000    -0.0156     0.0000 -0.0158   \n",
       "2016-01-26 100000.0000 1903.6300        0.0000     0.0141     0.0000  0.0140   \n",
       "2016-01-27 100000.0000 1882.9500        0.0000    -0.0109     0.0000 -0.0109   \n",
       "2016-01-28 100000.0000 1893.3600        0.0000     0.0055     0.0000  0.0055   \n",
       "2016-01-29 100000.0000 1940.2400        0.0000     0.0248     0.0000  0.0245   \n",
       "2016-02-01 100000.0000 1939.3800        0.0000    -0.0004     0.0000 -0.0004   \n",
       "2016-02-02 100000.0000 1903.0300        0.0000    -0.0187     0.0000 -0.0189   \n",
       "2016-02-03 100000.0000 1912.5300        0.0000     0.0050     0.0000  0.0050   \n",
       "2016-02-04 100000.0000 1915.4500        0.0000     0.0015     0.0000  0.0015   \n",
       "2016-02-05 100000.0000 1880.0500        0.0000    -0.0185     0.0000 -0.0187   \n",
       "2016-02-08 100000.0000 1853.4399        0.0000    -0.0142     0.0000 -0.0143   \n",
       "2016-02-09 100000.0000 1852.2100        0.0000    -0.0007     0.0000 -0.0007   \n",
       "2016-02-10 100000.0000 1851.8600        0.0000    -0.0002     0.0000 -0.0002   \n",
       "2016-02-11 100000.0000 1829.0800        0.0000    -0.0123     0.0000 -0.0124   \n",
       "2016-02-12 100000.0000 1864.7800        0.0000     0.0195     0.0000  0.0193   \n",
       "2016-02-16 100000.0000 1895.5800        0.0000     0.0165     0.0000  0.0164   \n",
       "2016-02-17 100000.0000 1926.8199        0.0000     0.0165     0.0000  0.0163   \n",
       "2016-02-18 100000.0000 1917.8300        0.0000    -0.0047     0.0000 -0.0047   \n",
       "2016-02-19 100000.0000 1917.7800        0.0000    -0.0000     0.0000 -0.0000   \n",
       "2016-02-22 100000.0000 1945.5000        0.0000     0.0145     0.0000  0.0144   \n",
       "2016-02-23 100000.0000 1921.2700        0.0000    -0.0125     0.0000 -0.0125   \n",
       "2016-02-24 100000.0000 1929.8000        0.0000     0.0044     0.0000  0.0044   \n",
       "2016-02-25 100000.0000 1951.7000        0.0000     0.0113     0.0000  0.0113   \n",
       "2016-02-26 100000.0000 1948.0500        0.0000    -0.0019     0.0000 -0.0019   \n",
       "2016-02-29 100000.0000 1932.2300        0.0000    -0.0081     0.0000 -0.0082   \n",
       "2016-03-01 100000.0000 1978.3500        0.0000     0.0239     0.0000  0.0236   \n",
       "\n",
       "            value_rebase  bm_rebase  value_estdev  bm_estdev  excess_return  \\\n",
       "Date                                                                          \n",
       "2016-01-04      100.0000   100.0000        0.0000     0.0000         0.0000   \n",
       "2016-01-05      100.0000   100.2012        0.0000     0.0000        -0.0020   \n",
       "2016-01-06      100.0000    98.8870        0.0000     0.0000         0.0131   \n",
       "2016-01-07      100.0000    96.5434        0.0000     0.0000         0.0237   \n",
       "2016-01-08      100.0000    95.4970        0.0000     0.0000         0.0108   \n",
       "2016-01-11      100.0000    95.5785        0.0000     0.0000        -0.0009   \n",
       "2016-01-12      100.0000    96.3243        0.0000     0.0000        -0.0078   \n",
       "2016-01-13      100.0000    93.9195        0.0000     0.0000         0.0250   \n",
       "2016-01-14      100.0000    95.4876        0.0000     0.0000        -0.0167   \n",
       "2016-01-15      100.0000    93.4251        0.0000     0.0000         0.0216   \n",
       "2016-01-19      100.0000    93.4748        0.0000     0.0000        -0.0005   \n",
       "2016-01-20      100.0000    92.3817        0.0000     0.0000         0.0117   \n",
       "2016-01-21      100.0000    92.8617        0.0000     0.0000        -0.0052   \n",
       "2016-01-22      100.0000    94.7453        0.0000     0.0000        -0.0203   \n",
       "2016-01-25      100.0000    93.2636        0.0000     0.0000         0.0156   \n",
       "2016-01-26      100.0000    94.5828        0.0000     0.0000        -0.0141   \n",
       "2016-01-27      100.0000    93.5553        0.0000     0.0000         0.0109   \n",
       "2016-01-28      100.0000    94.0725        0.0000     0.0000        -0.0055   \n",
       "2016-01-29      100.0000    96.4018        0.0000     0.0000        -0.0248   \n",
       "2016-02-01      100.0000    96.3590        0.0000     0.0000         0.0004   \n",
       "2016-02-02      100.0000    94.5530        0.0000     0.0000         0.0187   \n",
       "2016-02-03      100.0000    95.0250        0.0000     0.0000        -0.0050   \n",
       "2016-02-04      100.0000    95.1701        0.0000     0.0000        -0.0015   \n",
       "2016-02-05      100.0000    93.4112        0.0000     0.0000         0.0185   \n",
       "2016-02-08      100.0000    92.0891        0.0000     0.0000         0.0142   \n",
       "2016-02-09      100.0000    92.0280        0.0000     0.0000         0.0007   \n",
       "2016-02-10      100.0000    92.0106        0.0000     0.0000         0.0002   \n",
       "2016-02-11      100.0000    90.8787        0.0000     0.0000         0.0123   \n",
       "2016-02-12      100.0000    92.6525        0.0000     0.0000        -0.0195   \n",
       "2016-02-16      100.0000    94.1828        0.0000     0.0141        -0.0165   \n",
       "2016-02-17      100.0000    95.7350        0.0000     0.0143        -0.0165   \n",
       "2016-02-18      100.0000    95.2883        0.0000     0.0139         0.0047   \n",
       "2016-02-19      100.0000    95.2858        0.0000     0.0133         0.0000   \n",
       "2016-02-22      100.0000    96.6631        0.0000     0.0134        -0.0145   \n",
       "2016-02-23      100.0000    95.4592        0.0000     0.0134         0.0125   \n",
       "2016-02-24      100.0000    95.8831        0.0000     0.0129        -0.0044   \n",
       "2016-02-25      100.0000    96.9712        0.0000     0.0128        -0.0113   \n",
       "2016-02-26      100.0000    96.7898        0.0000     0.0124         0.0019   \n",
       "2016-02-29      100.0000    96.0038        0.0000     0.0122         0.0081   \n",
       "2016-03-01      100.0000    98.2953        0.0000     0.0132        -0.0239   \n",
       "\n",
       "            value_drawdown  bm_drawdown  \n",
       "Date                                     \n",
       "2016-01-04          0.0000       0.0000  \n",
       "2016-01-05          0.0000       0.0000  \n",
       "2016-01-06          0.0000      -0.0131  \n",
       "2016-01-07          0.0000      -0.0365  \n",
       "2016-01-08          0.0000      -0.0469  \n",
       "2016-01-11          0.0000      -0.0461  \n",
       "2016-01-12          0.0000      -0.0387  \n",
       "2016-01-13          0.0000      -0.0627  \n",
       "2016-01-14          0.0000      -0.0470  \n",
       "2016-01-15          0.0000      -0.0676  \n",
       "2016-01-19          0.0000      -0.0671  \n",
       "2016-01-20          0.0000      -0.0780  \n",
       "2016-01-21          0.0000      -0.0732  \n",
       "2016-01-22          0.0000      -0.0545  \n",
       "2016-01-25          0.0000      -0.0692  \n",
       "2016-01-26          0.0000      -0.0561  \n",
       "2016-01-27          0.0000      -0.0663  \n",
       "2016-01-28          0.0000      -0.0612  \n",
       "2016-01-29          0.0000      -0.0379  \n",
       "2016-02-01          0.0000      -0.0383  \n",
       "2016-02-02          0.0000      -0.0564  \n",
       "2016-02-03          0.0000      -0.0517  \n",
       "2016-02-04          0.0000      -0.0502  \n",
       "2016-02-05          0.0000      -0.0678  \n",
       "2016-02-08          0.0000      -0.0810  \n",
       "2016-02-09          0.0000      -0.0816  \n",
       "2016-02-10          0.0000      -0.0817  \n",
       "2016-02-11          0.0000      -0.0930  \n",
       "2016-02-12          0.0000      -0.0753  \n",
       "2016-02-16          0.0000      -0.0601  \n",
       "2016-02-17          0.0000      -0.0446  \n",
       "2016-02-18          0.0000      -0.0490  \n",
       "2016-02-19          0.0000      -0.0491  \n",
       "2016-02-22          0.0000      -0.0353  \n",
       "2016-02-23          0.0000      -0.0473  \n",
       "2016-02-24          0.0000      -0.0431  \n",
       "2016-02-25          0.0000      -0.0322  \n",
       "2016-02-26          0.0000      -0.0340  \n",
       "2016-02-29          0.0000      -0.0419  \n",
       "2016-03-01          0.0000      -0.0190  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_table(\"value\", tn)\n",
    "df = df.set_index(\"Date\")\n",
    "bm = yf.download(\"^GSPC\", start=df.index.values[0], end=df.index.values[-1])\n",
    "vd = df[[\"Value\"]].join(bm[\"Adj Close\"])\n",
    "\n",
    "# This is the main dataframe with daily data for each of the cient's transactions and values as well\n",
    "# as the values resulting from the long short pair transaction.\n",
    "vd.columns = [\"value\", \"bm\"]\n",
    "vd.index = pd.to_datetime(vd.index)\n",
    "\n",
    "vd[\"value_return\"] = vd[\"value\"].pct_change()\n",
    "vd[\"bm_return\"] = vd[\"bm\"].pct_change()\n",
    "\n",
    "# Get log returns\n",
    "vd[\"value_log\"] = qs.utils.log_returns(vd[\"value_return\"])\n",
    "vd[\"bm_log\"] = qs.utils.log_returns(vd[\"bm_return\"])\n",
    "\n",
    "# Rebase to 100\n",
    "vd[\"value_rebase\"] = qs.utils.rebase(vd[\"value\"])\n",
    "vd[\"bm_rebase\"] = qs.utils.rebase(vd[\"bm\"])\n",
    "\n",
    "# Exponentially weighted standard deviation.\n",
    "vd[\"value_estdev\"] = qs.utils.exponential_stdev(vd[\"value\"])\n",
    "vd[\"bm_estdev\"] = qs.utils.exponential_stdev(vd[\"bm\"])\n",
    "\n",
    "# Calculate excess returns over the de.\n",
    "vd[\"excess_return\"] = qs.utils.to_excess_returns(\n",
    "    returns=vd[\"value_return\"], rf=vd[\"bm_return\"]\n",
    ")\n",
    "\n",
    "# Drawdown details\n",
    "vd[\"value_drawdown\"] = qs.stats.to_drawdown_series(vd[\"value\"])\n",
    "vd[\"bm_drawdown\"] = qs.stats.to_drawdown_series(vd[\"bm\"])\n",
    "vd = vd.fillna(0)\n",
    "vd.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>valley</th>\n",
       "      <th>end</th>\n",
       "      <th>days</th>\n",
       "      <th>max drawdown</th>\n",
       "      <th>99% max drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.8372</td>\n",
       "      <td>-0.4784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.6771</td>\n",
       "      <td>-1.2271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-02</td>\n",
       "      <td>2017-02-02</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.6244</td>\n",
       "      <td>-1.5292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-09</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.5132</td>\n",
       "      <td>-0.4524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-02-23</td>\n",
       "      <td>2017-02-23</td>\n",
       "      <td>2017-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.5066</td>\n",
       "      <td>-0.4533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5788</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>2017-03-06</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.4362</td>\n",
       "      <td>-0.1652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-03-07</td>\n",
       "      <td>2017-03-07</td>\n",
       "      <td>2017-03-08</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0793</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>2018-05-18</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>74</td>\n",
       "      <td>-4.8597</td>\n",
       "      <td>-4.6388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>10</td>\n",
       "      <td>-2.7175</td>\n",
       "      <td>-2.2872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        start      valley         end  days  max drawdown  99% max drawdown\n",
       "0  2017-01-19  2017-01-20  2017-01-23     4       -0.8372           -0.4784\n",
       "1  2017-01-27  2017-01-31  2017-02-01     5       -1.6771           -1.2271\n",
       "2  2017-02-02  2017-02-02  2017-02-08     6       -1.6244           -1.5292\n",
       "3  2017-02-09  2017-02-15  2017-02-22    13       -0.5132           -0.4524\n",
       "4  2017-02-23  2017-02-23  2017-02-27     4       -0.5066           -0.4533\n",
       "5  2017-02-28  2017-02-28  2017-03-01     1       -0.5788            0.0000\n",
       "6  2017-03-02  2017-03-02  2017-03-06     4       -0.4362           -0.1652\n",
       "7  2017-03-07  2017-03-07  2017-03-08     1       -0.0793            0.0000\n",
       "8  2018-03-19  2018-05-18  2018-06-01    74       -4.8597           -4.6388\n",
       "9  2018-06-04  2018-06-07  2018-06-14    10       -2.7175           -2.2872"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drawdown period analysis.\n",
    "drawdown = qs.stats.drawdown_details(vd[\"value_drawdown\"])\n",
    "drawdown.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beta</th>\n",
       "      <th>alpha</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beta  alpha\n",
       "Date                    \n",
       "2020-12-23 0.0614 0.0004\n",
       "2020-12-24 0.0614 0.0004\n",
       "2020-12-28 0.0614 0.0004\n",
       "2020-12-29 0.0614 0.0004\n",
       "2020-12-30 0.0614 0.0004"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rolling greeks\n",
    "rolling_greeks = qs.stats.rolling_greeks(\n",
    "    returns=vd[\"value_return\"], benchmark=vd[\"bm_return\"], periods=252\n",
    ")\n",
    "rolling_greeks.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>bm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Start Period</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>2016-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>End Period</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2020-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Free Rate</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time in Market</th>\n",
       "      <td>0.1200</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative Return</th>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAGR%</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe</th>\n",
       "      <td>1.1800</td>\n",
       "      <td>0.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino</th>\n",
       "      <td>2.2300</td>\n",
       "      <td>1.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max Drawdown</th>\n",
       "      <td>-0.0700</td>\n",
       "      <td>-0.3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest DD Days</th>\n",
       "      <td>74</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volatility (ann.)</th>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R^2</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar</th>\n",
       "      <td>1.4600</td>\n",
       "      <td>0.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>4.5100</td>\n",
       "      <td>-0.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>89.9800</td>\n",
       "      <td>20.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expected Daily %</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expected Monthly %</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expected Yearly %</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kelly Criterion</th>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk of Ruin</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily Value-at-Risk</th>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expected Shortfall (cVaR)</th>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payoff Ratio</th>\n",
       "      <td>1.2700</td>\n",
       "      <td>0.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit Factor</th>\n",
       "      <td>1.9700</td>\n",
       "      <td>1.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Common Sense Ratio</th>\n",
       "      <td>39.7000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPC Index</th>\n",
       "      <td>1.4400</td>\n",
       "      <td>0.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail Ratio</th>\n",
       "      <td>20.1000</td>\n",
       "      <td>0.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlier Win Ratio</th>\n",
       "      <td>27.6700</td>\n",
       "      <td>3.6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlier Loss Ratio</th>\n",
       "      <td>2.7800</td>\n",
       "      <td>3.2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTD</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3M</th>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6M</th>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YTD</th>\n",
       "      <td>0.1900</td>\n",
       "      <td>0.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1Y</th>\n",
       "      <td>0.1900</td>\n",
       "      <td>0.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3Y (ann.)</th>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5Y (ann.)</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10Y (ann.)</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All-time (ann.)</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best Day</th>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worst Day</th>\n",
       "      <td>-0.0600</td>\n",
       "      <td>-0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best Month</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worst Month</th>\n",
       "      <td>-0.0500</td>\n",
       "      <td>-0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best Year</th>\n",
       "      <td>0.2700</td>\n",
       "      <td>0.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worst Year</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. Drawdown</th>\n",
       "      <td>-0.0200</td>\n",
       "      <td>-0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. Drawdown Days</th>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recovery Factor</th>\n",
       "      <td>9.0900</td>\n",
       "      <td>2.5100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulcer Index</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. Up Month</th>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. Down Month</th>\n",
       "      <td>-0.0300</td>\n",
       "      <td>-0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win Days %</th>\n",
       "      <td>0.5700</td>\n",
       "      <td>0.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win Month %</th>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win Quarter %</th>\n",
       "      <td>0.8900</td>\n",
       "      <td>0.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Win Year %</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.06</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 value          bm\n",
       "Start Period                2016-01-04  2016-01-04\n",
       "End Period                  2020-12-30  2020-12-30\n",
       "Risk-Free Rate                  0.0000      0.0000\n",
       "Time in Market                  0.1200      1.0000\n",
       "Cumulative Return               0.6800      0.8500\n",
       "CAGR%                           0.1100      0.1300\n",
       "Sharpe                          1.1800      0.7400\n",
       "Sortino                         2.2300      1.0200\n",
       "Max Drawdown                   -0.0700     -0.3400\n",
       "Longest DD Days                     74         214\n",
       "Volatility (ann.)               0.0900      0.1900\n",
       "R^2                             0.0100      0.0100\n",
       "Calmar                          1.4600      0.3900\n",
       "Skew                            4.5100     -0.7300\n",
       "Kurtosis                       89.9800     20.5600\n",
       "Expected Daily %                0.0000      0.0000\n",
       "Expected Monthly %              0.0100      0.0100\n",
       "Expected Yearly %               0.1100      0.1300\n",
       "Kelly Criterion                 0.2400      0.1000\n",
       "Risk of Ruin                    0.0000      0.0000\n",
       "Daily Value-at-Risk            -0.0100     -0.0200\n",
       "Expected Shortfall (cVaR)      -0.0100     -0.0200\n",
       "Payoff Ratio                    1.2700      0.9600\n",
       "Profit Factor                   1.9700      1.1700\n",
       "Common Sense Ratio             39.7000      1.0000\n",
       "CPC Index                       1.4400      0.6300\n",
       "Tail Ratio                     20.1000      0.8500\n",
       "Outlier Win Ratio              27.6700      3.6900\n",
       "Outlier Loss Ratio              2.7800      3.2600\n",
       "MTD                             0.0000      0.0300\n",
       "3M                              0.0800      0.1100\n",
       "6M                              0.0800      0.2200\n",
       "YTD                             0.1900      0.1500\n",
       "1Y                              0.1900      0.1600\n",
       "3Y (ann.)                       0.1600      0.1200\n",
       "5Y (ann.)                       0.1100      0.1300\n",
       "10Y (ann.)                      0.1100      0.1300\n",
       "All-time (ann.)                 0.1100      0.1300\n",
       "Best Day                        0.0900      0.0900\n",
       "Worst Day                      -0.0600     -0.1200\n",
       "Best Month                      0.1000      0.1300\n",
       "Worst Month                    -0.0500     -0.1300\n",
       "Best Year                       0.2700      0.2900\n",
       "Worst Year                      0.0000     -0.0600\n",
       "Avg. Drawdown                  -0.0200     -0.0200\n",
       "Avg. Drawdown Days                   8          17\n",
       "Recovery Factor                 9.0900      2.5100\n",
       "Ulcer Index                        inf      1.1300\n",
       "Avg. Up Month                   0.0500      0.0400\n",
       "Avg. Down Month                -0.0300     -0.0300\n",
       "Win Days %                      0.5700      0.5600\n",
       "Win Month %                     0.8700      0.7200\n",
       "Win Quarter %                   0.8900      0.8500\n",
       "Win Year %                      1.0000      0.8000\n",
       "Beta                              0.06           -\n",
       "Alpha                              0.1           -"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = qs.reports.metrics(\n",
    "    returns=vd[\"value_return\"], benchmark=vd[\"bm_return\"], mode=\"full\", display=False\n",
    ")\n",
    "metrics.columns = [\"value\", \"bm\"]\n",
    "metrics.head(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_return</th>\n",
       "      <th>bm_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.0770</td>\n",
       "      <td>0.1798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.0300</td>\n",
       "      <td>-0.0499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.2505</td>\n",
       "      <td>0.2616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.1813</td>\n",
       "      <td>0.2028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value_return  bm_return\n",
       "Date                         \n",
       "2016        0.0000     0.1149\n",
       "2017        0.0770     0.1798\n",
       "2018        0.0300    -0.0499\n",
       "2019        0.2505     0.2616\n",
       "2020        0.1813     0.2028"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yearly returns\n",
    "year_return = pd.concat(\n",
    "    [\n",
    "        qs.utils.group_returns(vd[\"value_return\"], vd.index.year),\n",
    "        qs.utils.group_returns(vd[\"bm_return\"], vd.index.year),\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "year_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value_return</th>\n",
       "      <th>bm_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0258</td>\n",
       "      <td>-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2018</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.0469</td>\n",
       "      <td>-0.0256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0991</td>\n",
       "      <td>0.0772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0536</td>\n",
       "      <td>0.0133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0465</td>\n",
       "      <td>0.0336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2020</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0932</td>\n",
       "      <td>0.0463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.0088</td>\n",
       "      <td>-0.0262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0969</td>\n",
       "      <td>0.1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           value_return  bm_return\n",
       "Date Date                         \n",
       "2016 1           0.0000    -0.0346\n",
       "     2           0.0000    -0.0028\n",
       "     3           0.0000     0.0645\n",
       "     4           0.0000     0.0031\n",
       "     5           0.0000     0.0157\n",
       "     6           0.0000     0.0024\n",
       "     7           0.0000     0.0352\n",
       "     8           0.0000    -0.0011\n",
       "     9           0.0000    -0.0004\n",
       "     10          0.0000    -0.0194\n",
       "     11          0.0000     0.0341\n",
       "     12          0.0000     0.0183\n",
       "2017 1           0.0149     0.0179\n",
       "     2           0.0362     0.0366\n",
       "     3           0.0258    -0.0001\n",
       "     4           0.0000     0.0092\n",
       "     5           0.0000     0.0118\n",
       "     6           0.0000     0.0050\n",
       "     7           0.0000     0.0193\n",
       "     8           0.0000     0.0009\n",
       "     9           0.0000     0.0192\n",
       "     10          0.0000     0.0221\n",
       "     11          0.0000     0.0279\n",
       "     12          0.0000     0.0099\n",
       "2018 1           0.0000     0.0550\n",
       "     2           0.0000    -0.0370\n",
       "     3          -0.0469    -0.0256\n",
       "     4           0.0000     0.0039\n",
       "     5           0.0423     0.0218\n",
       "     6           0.0346     0.0051\n",
       "     7           0.0000     0.0357\n",
       "     8           0.0000     0.0300\n",
       "     9           0.0000     0.0044\n",
       "     10          0.0000    -0.0696\n",
       "     11          0.0000     0.0191\n",
       "     12          0.0000    -0.0929\n",
       "2019 1           0.0991     0.0772\n",
       "     2           0.0000     0.0296\n",
       "     3           0.0000     0.0183\n",
       "     4           0.0000     0.0387\n",
       "     5           0.0000    -0.0671\n",
       "     6           0.0150     0.0672\n",
       "     7           0.0536     0.0133\n",
       "     8           0.0000    -0.0161\n",
       "     9           0.0000     0.0173\n",
       "     10          0.0213     0.0210\n",
       "     11          0.0465     0.0336\n",
       "     12          0.0149     0.0284\n",
       "2020 1           0.0000    -0.0011\n",
       "     2           0.0000    -0.0854\n",
       "     3           0.0000    -0.0966\n",
       "     4           0.0000     0.1265\n",
       "     5           0.0932     0.0463\n",
       "     6           0.0000     0.0219\n",
       "     7           0.0000     0.0544\n",
       "     8           0.0000     0.0681\n",
       "     9           0.0000    -0.0375\n",
       "     10         -0.0088    -0.0262\n",
       "     11          0.0969     0.1034\n",
       "     12          0.0000     0.0290"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yearly/Monthly returns\n",
    "month_return = pd.concat(\n",
    "    [\n",
    "        qs.utils.group_returns(vd[\"value_return\"], [vd.index.year, vd.index.month]),\n",
    "        qs.utils.group_returns(vd[\"bm_return\"], [vd.index.year, vd.index.month]),\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "month_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_return</th>\n",
       "      <th>bm_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1145</td>\n",
       "      <td>0.1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0362</td>\n",
       "      <td>-0.0638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.0215</td>\n",
       "      <td>-0.0766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.0246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0501</td>\n",
       "      <td>0.1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0546</td>\n",
       "      <td>0.1691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0066</td>\n",
       "      <td>-0.0745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.2394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0142</td>\n",
       "      <td>-0.0115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value_return  bm_return\n",
       "Date                         \n",
       "1           0.1145     0.1161\n",
       "2           0.0362    -0.0638\n",
       "3          -0.0215    -0.0766\n",
       "4           0.0000     0.1882\n",
       "5           0.1429     0.0246\n",
       "6           0.0501     0.1001\n",
       "7           0.0546     0.1691\n",
       "8           0.0000     0.0818\n",
       "9           0.0000    -0.0008\n",
       "10          0.0066    -0.0745\n",
       "11          0.1500     0.2394\n",
       "12          0.0142    -0.0115"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_return_agg = pd.concat(\n",
    "    [\n",
    "        qs.utils.aggregate_returns(vd[\"value_return\"], period=\"month\"),\n",
    "        qs.utils.aggregate_returns(vd[\"bm_return\"], period=\"month\"),\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "month_return_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_return</th>\n",
       "      <th>bm_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1300</td>\n",
       "      <td>-0.0351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2001</td>\n",
       "      <td>0.3394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0546</td>\n",
       "      <td>0.2637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1740</td>\n",
       "      <td>0.1339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value_return  bm_return\n",
       "Date                         \n",
       "1           0.1300    -0.0351\n",
       "2           0.2001     0.3394\n",
       "3           0.0546     0.2637\n",
       "4           0.1740     0.1339"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarter_return_agg = pd.concat(\n",
    "    [\n",
    "        qs.utils.aggregate_returns(vd[\"value_return\"], period=\"quarter\"),\n",
    "        qs.utils.aggregate_returns(vd[\"bm_return\"], period=\"quarter\"),\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "quarter_return_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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U19drtzctibBYLJAkCYqiQBRbPtYQGNUeNWoULBYLOnXqhL59+2Lv3r3o3r170H2rq6vx5ptvapcfe+yx0z62w+HAwIED8c477+DBBx/UvlB4PB7teT0ejxY+9aqpqYHD4dDatHPnzqDRa0VRkJWVpd0vcN8z5XQ6MXfuXAwZMgT9+/fXrrdarUGBP/BzS74wERGFEsMzEUWVAwcOoKSkBC+//DIAf6gSBAHHjh1rts724MGD2kloHTt2hCAI+O///u9Wb3OghEIQBO26pj83lZiY2GxgPpGiKHA6nfB4PHA4HIiLi0NpaakWzI8ePYqUlJQzeszdu3eja9euAICEhATk5OTgqquu+tX9amtr4XK54Ha7z3jmEJfLhXnz5iE7OxsjR44Mui0lJQWlpaXo27evtg2xsbEs2SCisGPZBhFFlTFjxuDee+/F7NmzMXv2bGRnZ2Pw4MGYMmVKs7/r9XohiiJiY2OhKApWrVp1xuUMp6IoijZKraoqJEmCLMsAgKSkJHTt2hWrV6+GJEkoKyvDjh070KtXrxY9165du1BeXg5VVeF0OrFs2TKkpqZqo78DBgzADz/8AJfLhfLycmzevPmUtcYnbkNVVRW+/fZbFBYWYtSoUQD8pS579uxBQUGBtp2FhYWoqalBfHw8evbsicWLF8PlckGWZRw8eLDZ5/J4PJg3bx66dOly0pMABwwYgC1btqCsrAxutxurV6/WtQ1ERK2NI89EFFVsNltQCYLZbIbVaj1t2UBglLd79+7o0aMH3njjDVitVgwbNkybG/ls/fLLL1i4cKF2+fnnn8eAAQMwdepUAMA111yDr7/+Gi+99BJiY2MxZswYbYaOM1VTU4Ply5fD6XTCarUiKysLM2bM0G4fPXo0Fi9ejP/5n//R5nk+3Uwbhw8fxl/+8hcAQExMDLKysnDHHXdoo9WJiYmYOXMmvvvuO3z55ZcQRREZGRmYOHEiAODqq6/GsmXL8Oabb0KWZWRlZWkzmJzKrl27UFJSgrKyMmzdulW7/u6770ZiYiJ69OiBESNG4KOPPtLmeR49enSL/l5ERKEkqKqqhrsRREStZdmyZVBVFePHjw93U4iIyABYtkFEhuV2u7Fv3z5d8zQTERHpwbINIjKkPXv24P/+7/+QnZ2tnXRGRER0tli2QURERESkE8s2iIiIiIh0YniOYk6nM9xNoBBhXxoD+9E42JfGwb40jkjpS4bnKBaq+Wkp/NiXxsB+NA72pXGwL40jUvqS4ZmIiIiISCeGZyIiIiIinRieiYiIiIh0YngmIiIiItKJ4ZmIiIiISCeGZyIiohb4+WBVuJtARGHA8ExERNQCmw9Xh7sJRBQGDM9EREQtoKpquJtARGHA8ExERNQCMrMzUZvE8ExERNQCHHkmapsYnomIiFpAYXYmapMYnomIiFqAI89EbRPDMxERUQvIDM9EbRLDMxERUQswOxO1TQzPRERELcCRZ6K2ieGZiIioBZididomhmciIqIWkDndBlGbxPBMRETUApxtg6htYngmIiJqAa4wSNQ2MTwTERG1AEeeidomhmciIqIW4GwbRM1bc6Ay3E0IOYZnIiKiFmB2Jmre9pKacDch5BieiYiIWoCTbRA1TzHgt0yGZyIiohZgzTNR84x4Yi3DMxERUQuw5pmoeYoBD9EwPBMREbUAszNR81i2QURERAA48kykhwEHnhmeiYiIWoLZmah5HHkmIiIiAIBsxCE1ohAz4hEahmciIqIWUGG8UEAUagbMzgzPRERELSEr4W4BUeQz4hEahmciIqIW4DzPRM1jzTMREREBMOYsAkShZsT3CcMzERFRC7Dmmah5HHkmIiIiAKx5JtKDKwwSERERANY8E+lhxO+YDM9EREQtYMT5a4lCjSPPREREBMCY89cShZoRv2QyPBMREbWAEUMBUagZ8W3C8ExERNQCRgwFRKFmxC+ZDM9EREQtYMRQQBRqrHkmIiIiAJxtg0gPA2ZnmMPdgNb2yiuvYP/+/TCZTACA+Ph4PP/886isrMR7772Ho0eP4qKLLsJ1112n/c7cuXPRv39/DBw4MEytJiKiSKcYcQ4uohAz4iIphg/PADB9+nSMGjUq6LqlS5eid+/euP/++/H888/jwgsvRFZWFgoKClBbW8vgTEREp2XEUEAUakZ8n7TZso3y8nL06tULdrsdmZmZKC8vhyzL+OKLLzBz5sxwN4+IiCIcB56JmmfElTjbxMjzwoULsXDhQnTq1AlTpkxB7969kZ6ejt27d6Nbt244ePAgrrzySnz33XcYPHgwkpKSWvQ8TqcTHo8nxK0/NZfLhcrKynP2fNR62JfGwH40Dj19KUsy+zsK8H0ZXl6fL2R//3Pdl6fKg4YPz9OmTUNaWhpMJhM2btyIN998E0888QQmTJiAjz/+GC+//DJGjRoFm82GrVu34v7778fHH3+MkpIS9OzZE1OnTtX9XLGxsYiNjW29jTlBZWVli4M+RRb2pTGwH41DT1+qgsD+jgJ8X4aXaDoUsr9/pPSl4cs2unXrBrvdDovFguHDh6NHjx7Yvn07YmNjceedd+LPf/4zLr/8cvzrX//Ctddei7Vr10JRFDz00EMoLCzEjh07wr0JREQUgQxYykkUcqx5NgBBEH513ZYtW5CYmIju3bujuLgYmZmZEAQBmZmZKC4uDkMriYgo0nGeZ6LmMTxHmfr6euzcuRM+nw+yLOPnn3/G3r170a9fP+0+brcbS5YswdVXXw0ASE5Oxp49eyBJEgoKCpCcnByu5hMRUQQzYCYgCjnO8xxlZFnGwoULUVpaClEUkZqait///vdITU3V7rNo0SJcdtlliImJAQCMHDkS7733Hv74xz+if//+GDRoULiaT0REEcyII2pEobJ89zGM7d3RkCsMGjo8x8fH47HHHjvtfZoujgIADocD9913X2s2i4iIDMCAmYAoZPZX1AMwZnmTocs2iIiIWgtHnolOTYX//WHEtwnDMxERUQswPBOdWuDIDEeeiYiICACgGHDlNKJQCWRmI9Y86w7Pe/fuxerVq6GqKvLy8nDHHXfgkUceQXV1dWu2j4iIKCIpMF4oIAoVtSE9G/E7pu4TBu+8804UFRWhoKAA11xzDfLz8yEIAkpKSjBv3rzWbCMREVHEMeCAGtFZkRUVNW4f2sdYta+WbXrkeceOHRg2bBiKi4uRn5+P//qv/0JOTg6+++671mwfERFRRFINWMtJdDZ2H6vDfQv8KzMHMrMRzw3QHZ5ra2uRkJCAvLw8CIKAu+++GyNGjMDx48dbsXlERESRR1VVjjwTnUBRVIgNKzlrZRsGfJ/oLtvo3LkzvvzyS6xduxYdOnRA586dcezYMa7AR0REbY6iAkK4G0EUYRS1MTw3HXlWVRWCYJx3jO6R53vuuQdlZWXYtm0b/vCHP0BRFKxevRoXXHBBa7aPiIgo4qgARIGlG0RNyaoKU0OybDrPs9FGn3WPPN9///2YPHkyfD4fevfuDUVR8PPPPyMhIaE120dERBRxVFWFKApQwRFoogBFQZOyDf91asM/Izmj5bm7d++OyspKHDp0CAAgCAJqa2vRvn37VmkcERFRJFJVQITgDwhMz0QA/CPPjWUbgZFnteEIjXHeKLrD8/fff4/bb78dRUVFQdcLggBJkkLeMCIiokilqCoEwXgjakRnQ2latqE2/m+06ibd4Xn27NnaiHNTrPciIqK2SBQEcOiZqFFQ2UbDdUYs29B9wmBpaSmGDRuG/fv3o7KyElVVVdo/IiKitkRRAVE0XiggOhuyqsL0q6nqVMMNtOoeeZ42bRqOHDmCrKysVmwOERFR5FOhQuSIM4WYT1ZgMeke14w4SsOJtP6fG683VnQ+g/BcX1+P77//Hjk5ORg4cCDMZv+vCoKA999/v9UaSEREFGlUFf7ZNoyWCiisJv3tZyy8PRd2iyncTWkR/yIp/p9VtXGqOqO9T3SH5y+//BKAf5nuHTt2aNczPBMRUVujqg3zPIe7IWQoTo8c7iacFVmFVrahNLneaEt06w7PTz31VGu2g4iIKGoEVlIzWi0nhZco+uuGo1XTso3Ae8NACwtqdIVnSZLQrVs3pKamYuzYsa3dJiIioogWWGGQKJREQYCiNH+/SCUpKiwn1Dxrk9IYiK6qdLPZjNmzZ2PBggWt3R4iIqKIpwZGnsPdEIM7VOXCqn0V4W7GOSMKQlSPPEuyArMYvMKgAMFwZRu6T+mcPHkyNm7cCCWavxIRERGFQGDk2WCZIOJ4JBkub3TXAZ8JkxjdQVNSVKw7WAVJVqA2fLUUYLxzA3TXPCuKgs2bNyM7Oxu5ubmw2WwAeMIg0blS55EQZ9P9liU6KwVlTphEAd06xIS7KRFJUf2ff6rhYkFkUVTjnWx2OiZRgKJE7/bKiopjdV7U+2TUN3zpabM1z0DjbBv79u3Dvn37tOsZnonOjbkbD+P3I7LC3QwyqP/bfgRT+6dpl8ucHlhEkeH5JJbsOooLu7TjyPM5ICtq2wrPgn/GimgVyP07S2uxqagagD8nGq0PdYfnJ598EoIRvz4QRQlJZskUtZ6CMmfQZVlRIQrG+sALlSeX5GPxHUO1ZYip9SiqijMZiF2efwxjszu2XoNamSgIkKN45FlpOBLjkxu/9Agw3pdM3eF5zpw5rdgMImqOFMU7VIp8tR4p6DLD8+n5a565SEprk1VVd5j8fk8Zfimuicrw7PbJWLLrGMQor3kONF2SlcbwbMD50HWH52eeeeak1wuCgD//+c8haxARnZyv4VheaY0bqQl27XqvpKDc6UV6ov1Uv0rUrJqThGeTaLSPvNDxz7YR7la0zL5yJyrqvcjt2h4AsKesDt07xMIUIRtUUu3W9meKor/muaDcieMuX2s2rdUcd/vw93UHYTOLUT3bhqKoMIkCJEWFrDaZlSaKt+lkzmjk+VRlGwzPRK1PapjpZsH2I7hzeJb2QVdR78W6g5WYlpMezuZRFHL5ZNS6JXSMt6HuhPAsqSpMiopqlw+JDkuYWhi5tJHnKBxTc3pl1Lkb+/v3n/2Cv88cGDH17V9uK8G9I88D4A/OemuAZUXFcXd0hmdF8b+eon2eZ0UFzKIAn6JCbSi5MeK5Abqnqrvpppu0f9dffz0GDBgAVVUxZcqU1mwfUZv3+89/AQD4Gg5dSkpwCYdPViFF8c6WwqfouAtbS/wn9Tg9ctDhcVlR4ZVVzN9cHK7mRbTASmrRGApkRQ3ah5hNAj7edDiMLQrmbDI1naKqumefkBQVdVG6vLXUMGIritE9z7MC1R+eZQVKk5FnJQq/ZJ6O7pHnDz/88FfXzZgxAyaTKZTtIaImfj5YhY1FxwEAvoYTBiVFgaQosDV895UUJapPMKHwkZXGelKPrMArK3CIJu02RVVYa38SJsEfdsQoreX070MaWy4KQlBgDbc6b+Oo+Mmmqvu/7UcwNrsjYqzB+UNSVHiiaCShsLIeWUn+0X6vrMBqFmGK8pkpVBUwCYJWZiirgMUU3SdBnozukecT1dXVwe12Y9myZaFsDxE1sbawSvs5sPORZRWSHDzyHLjt27yj57aBFNWkJq8dr6QEBQ9ZUeGRZPii+RhyKzGJ/nAQrbNtnDjybBIF1Hul0/zGudU0yPu/xAXfXlXvg/cksw9Jsv81Gy2un7sJNQ1lJl5Jgd0sQhQQ9fM8B2qeA5ftFpMWpo1Cd3g2mUxB/xITE/HNN98gKSmpNdtH1Kb5mnxABHY+khr8wSc1GT0sKA+eboyoqa9+KcHnW0u0y3KT15LcZNSuoNyJtYVVcEsKZIN96IWCSRQgyUpDLWf0/X2kk4VnX2SETkVVtcU1ApdPHImVFDVo3xggq9E18hxnM+PlFf51M7yyAlEQYDWJUT3Ps6oGwnPjkVK7WTTcESzd4VlV1V/9y8jIwFtvvdWa7SNq07yyAlPD4JZWtiGfEJ5lRauRc/ui54ODzr06j4ySard2uenIs80swtsQPGrdEsqdXngkRau1p0ZNR56j8a8TGHn+bIu/nt0kIGJC58LtpUH7MVn1h+JFO0uxZJf/yJo/PP/6Ly/JSsRshx5xNjMKK+uhNIR+n6wg3m6O6pFnwH+CYODoqKSosJlNhlunQHfN84EDB4Iux8bGIjk5OeQNIqJGPlmFxRSobW7cGUlNDqU3HXmOpkOWdO7ZLSa4pMbwXO70Qm44K95qFuFp+IDzygrqPBK8kmK4D71QMAlCY81zFOYcuWEf8urK/Zg+KKNhJD0yNiTvaC3cTfZjiqKi2iVBUVSUVLsxoU8nf3g+STlRtNU8x1nNOFLjhtvnD/2yoiLOZo7qmmfAX0PfdLDHbhHbbtnGrbfeisWLFyMzMxOZmZlITk7GggUL8Ic//KE120fUpkmyAmtDeNbKNk442ccnN54w6G744FBPOPRJBAAOixg0qvfPDUWQGka9Eu0WLXj4ZAV1Xpkjz6dgNonwKQqEKB15lhQ1qBxHFISIOax+3OXT9mOAv2wj/1gdDlTUwyU1lgIE9odNy2b8C/tEz5zC8XYTqlw+1PtkeGX/6ynGYorq2TYAQGyYqg4AfIoCh8VkuHMndIfnlStXoqCgIOi6FStW4M033wx5o4jIz6eosJgbZ9Uod3rx4/7KoFEi/2T0/tHDQPjZV1GPO/61NRxNpijilvzT01W7JaTEWbWyDa+sos4jwSNxto2TMWtlG4jKoWdJUeGWGkvCTKJw0hricKh2SXD7mtY8++cjr/FI2kCC/6Rpf3vf//mQdl9JUeGwRs/JaZ3ibAD8qwt6Jf+KfHaLGNXzPAP+YBn4jJIVwG423shzs2Ubt912m/bz8uXLtcuKouDbb7+Fw+FovdYRtXE+WYG14RNOUlRU1Xvh9MpBIxOB2Tbu/Wo7TA1n/3skJWJWC6PI0fT1BEA7VFzrlpAca9VmMPDJCpyBkecICVWRpPGEwegdea73yrCZTVBV/7y8kfAlSVFV1Lh9QdOaKaqKep8MAUBaw8qqktpY83yw0oV6r4yvd5RCUlTEWkzwSP5p3yJdx3h/eHb5ZG0ZckOMPDc5kiEpCmwGPGGw2fD84YcfQhAECIKAvLw85OXlBd0+fvz4VmscUVvnk1VYTSLUhg8Ll6+xjiwgUPP888HjyO3aDgDg9EqItXIOdgrmlVRt9A7wn2DqH3n2ITnOqh0uD4RojjyfXGAFtWieqi6wj5AV1T8vbwT0s38fJ8MkCthWUo0B6YmQFRUurwxFUWEJDCTIjTXPR2s9qKz3Yv7mw+jWIRaxNhM8kox4/ad0hdX/XN0PLp8CRVHhU1Q4LKaor3kWBGj9Iykq7G3xhMGnnnoKAPD0009j6NChWlg2mUzo0qULrrvuutZtIVEb5pMVxDQchpQUBa6Gw5knq3kOLNwAADVuCQl2LqlMwXyyAnOT8BwYea5xS+gYZ4NXUrCnrA5vrPafIO6VlYg5kSySBMocovaEwYZzIuLtZnhkBWLDSHq4eSUFLp9/n7dweykGpCdCUf2B2iv7j6b9a0sxFu4oxRXZKQCACqcXLp8Mi0mEoqqItZq0E18jnQAgzmaC0ytBVv3nuNgtpugv2xAaT0DVThiMgC9noaQ7PAPAsGHDMG7cuFZtUKjV19dj3rx52LFjBxwOByZMmIDRo0ejsrIS7733Ho4ePYqLLroo6EvA3Llz0b9/fwwcODB8DScCEG83I9Zmhq/hBJnG8Ozfu9Z5JP/sGw2zJTi9MnyygqIqFzo1HBIkCvA2lG2oqr/O2dMwzWGtR0JGoh2ltR64vDKqXP6FG1i2cXJmbbaNKC3bkFU4vTLibWZ4G2qfI+EIg0dWUO+TkRJnxaEqF4DGmmcVQEWdF59s8k+vFwhnNR4JNW5/PbRZFGA1maNixo3CynpIiopEuwXPLtuDrKQYSIqKGIsY9WUb5iY19NoJgwbbj+guCnrqqafgcDjwm9/8BkOHDkVxcTGeeeYZrF+/vjXbd9bmz58PWZbx0ksv4Z577sGiRYuQn5+PpUuXonfv3njhhRfwyy+/oLCwEABQUFCA2tpaBmeKCD2TY5HbtR18sn9RgKYjz5Ks4P11hyAp/kN+VpOI/GN1eH/dIbglBXEs26ATBFb/qnZL+FfDHL+SAq1swyPJsFsaXzceSWbt/Elo8zyLAtQojM+yqsItNY48R8pUdYFVLmOtZhyqcmH9oSrUeiR4ZX/ZWtFxN+wNtcw+xT+NokUU/OHZLCLRYUFGO3tUhOfFeUdRWutBgt2Mg1Uuf4mUbIyyjaY1zpKstu1FUhYuXIhLL70Un376KTZu3IhOnTrh7bffxksvvdSa7TsrHo8HmzdvxpQpU2C329GlSxdcdNFFWLNmDcrLy9GrVy/Y7XZkZmaivLwcsizjiy++wMyZM8PddCKNxSQ0BGcF3+0pB+DfIdV5ZdR6JDg9MqxmUZsP+sMNRTh83BXOJlMEE+A/u7/a5V+OWVIUuLwy2tkt8EjBq7l5JEV7XVEj7YRBAFGYnbXFcRJsZngl/wh6JEwlFqi1j7X5p3D729qD2Flaq93ukWQI2qJRKqpcPqQn2vHIojxYTQJS4224KCspKsLzsVoPyuo8SLD5CwD8pQ0KHFZT1C+SYmsyu4asGnN5bt0V9c888wySk5PRp08frF69GmazGRdffDHWrVvXmu07K0ePHoWqqkhPT9eu69KlC3bt2oXevXtj9+7d6NatGw4ePIgrr7wS3333HQYPHhw1S44fqHLji92F4W4GhYDL5YLDUaNdVlRgQEYCKut9SEu0492fDqLWI2H1vgoAwCNf5+Glq87HxqLjEAVgwfZS2BpGZGRFRfsYC2o8Et77qTAcm9NmndiPoZbdMQ57y50t/nA9WOVC+xgLDh93Y0NRFWKtJpTWeGASBTgsJuw+Wov+afEAgJHdO2D1vgqM6ZHcJl9Hp+rLrKQYJNjN2FZSgwS7GfO3FCPWEl1Heb7bU452Dgs6xdvw0fpD2jzf7/5UiHAeZ6h2+09i7NgwhVtKnA2rC/z7vG5JMTCbRKTE2nDouAuL845ChYpeKXHYVlIDRfUH0nYOCz7bUoz1B6u0x23t92VLOL2yv9SkySJYaQl2dIyzYd7Gwygod4a5hWfOZjZhzYEqpCbYsL3E//f+29qDmDkoA9/tKUN5neesHv/K8zshJhQNDQVVp9jYWPXOO+9U77//flUURVVVVXX27Nmqw+HQ+xDn3J49e9QHHngg6Lrt27erjz/+uFpXV6e+++676jPPPKP++9//VsvLy9UXX3xRdbvd6rx589SXXnpJXbBgwRk934QJE1T4xyFUAOqdd96p3nnnnUHXjRo1Sp0zZ44aFxenXZeWlqbOmTNHHTx4cNB9H3zwQXXWrFlB102aNEmdM2dO0HW9evVS58yZo/bq1Svo+jlz5qiTJk0Kum7WrFnqgw8+GHTd4MGD1Tlz5qhpaWnadXFxceqcOXPUUaNGcZvCsE3TZ12vXvbYe0HXJQ+bpN72/LtqTOfG37ckdFD/POcZNW3sLUH3vXb2wxG3TUbsp3O9Td0vHBXSbbr2hY/VXtPuDf79629Xb3n4GfZTM9vU4/Lp6l3Pvh6123THnXeqM2b/MeL66cT9XsehV6pDXlkVtE2WhA5q7sv/UXtfdk3Qfaf+/tGofe3Z4turT8x5NqrfT8Nf/u5Xn09DXlmlXjhmXNRu06kIqqqvuKZnz55o164dhg8fjjfffBNlZWXIzc2FyWRCfn6+noc45w4dOoQXX3wRb731lnbdhg0bsGzZMjzxxBNB933rrbcwbtw4FBUVoaioCDfccANee+01XH755ejXr9+5broulZWVUTNKTqd3Yl+qqoqZ/9yE567sjU1F1Xhl5T4AwJPjeuG8DrHomxqP33++DRuLqrHhwZG48P+txn9PPh//tSgPP913MQ+1h0lrvycDI8B3XpTV4seQFRX3LdiOrcU1mDU4A9/mHcWb1+YgKykGDy3ciZ2ltXh2Qm8M6doOF/6/1djw4MjQND7KnKov315zABaTiG0Nf7+LukXfPvjtNQfw+xHdcKCiHvct2I4KpxezR2ThxiFdwt00PLooD5f2SoYkq1h3sAo/7K9AnUfGhgdH4vZPt+KNaf0x6n/X4PmJvbFweykevawnPtpQhC3F1fjd8EyM7d3xV48Z6Z+VeaW1eHXVPrwxrX/QOQfRZsrff0ZJjQdPjuuFv689iJIa/0jz32YMwMCMxJA8R6T0pe5P2GuuuQabNm3C22+/DQDIyMjAgQMHcO2117Za485Wp06dIAgCjhw5ol1XVFQUVMYBAFu2bEFiYiK6d++O4uJiZGZmQhAEZGZmori4+Fw3mwiCIKCy3oekGCucXkm73iwK6JvqP6x+wwVd8PktQ7TbAnOgMjgbl66RjmaYRAEun4I4mxkWUUC9T0aC3V/BV+eRUOP2wWziSYKnIiuASRCgqA0rDEah34/oBgBIirGgX1oCBnVOjJgTQ2OtJu08Dm/DVIoBZlHQFj8Z3LkdShvKNJ4Y2wvdkmKidt9nNgnwSErUzhseEJjhaXLfVDStLIvurTq5M5ptY8aMGZBl2b8MsMeD6dOn4/HHH2/N9p0Vm82GwYMH4+uvv4bb7cbhw4fx008/YcSIEdp93G43lixZgquvvhoAkJycjD179kCSJBQUFCA5OTlczac27rjLh3YOC1LibMhs71/Js+kH3IjzkpCV1FgBZo3SDw7ST1EBMQQhx+2T0c5uxvVDOsPlUxDfcNLSa9P6o2dKnLZSJf2aPzT7p6gTovzvFG83Y9agDPxxdA+YI2Rb4mxmOL3+eZs9shIUwuxm/3R0z07oDbtZRIXTizibf6TWv3pmdO4DzaIAr6SE5L0dTumJjStONz3x2B0FJ3CeKd2vNIfDgfnz5+PYsWNYv349jh07hltvvRWTJ09uzfadtVmzZgEAHnnkEbz++uuYNGkSsrOztdsXLVqEyy67DDEx/hAycuRIOJ1O/PGPf0T79u0xaNCgsLSb6E+X94RJFHBVv1Rc1su/IMCpRibG9EiGxSTgxUl9zmUT6RwLrAZ3ttySgsykGMRazbh2QJo2Ymcz+xeaCHxJ+/6u4Wf9XEbj/wIDqCoQIXmzxURBQP/0BJhEIWJGngekJyAj0Q6byR8om4awGKv/S974Ph1hN4uwmUXtC4xXVqP2iIlZFP2LwERn8zVjejYONjYtCDZieG52to1jx47hgQcewLZt2zBgwAC88cYb8Pl8mDBhAjZv3nwu2nhWYmJi8Lvf/e6Ut5+4QqLD4cB9993X2s0iata0nDTt58D8zqf6tvvSVeejvM7DhVEMLjDqedaPo6h4YNR5AICHxvQIuk2SG8MzV6n8NUX1f4FRVdUwh6NF4dRfzM+1SxsGCjYVHYeiqnjs8p4orPRPvRnbZO56s0lEcqxVuxztI88+WY36IxmjunfQfg5kZ5tZRKoBP5eaDc8PPvgg5s+fDwDYtWsXSkpKsH79erhcLpjNZtx4442t3kiitq7e2xCeTzM6lBxnvB0UBZNVFWII8oHNLCLefvLdv6yqMEfIKGQkUhpWFjSJQsQEzrPVIdaKnPSEcDcjiM0swm4xYVLfVO06xwkn0zUdLPDJKqxROnRrFgXDLSISOGIwpV8qsjvGhbk1odfsbvg///kP0tLS8OGHH+Luu+/GqlWr4HK5cOutt6KgoADvv//+uWgnUZuWlmCDzSwa5sOaWkZREJLD6w6LCTGnOKvfYTFFzCH8SKSo/pHawIlrRuCwmNA9OTbczQhiMYm/CsuZSY6gy6nxdu1nn6zAEqV9YjYZLzxH+SKJzWr2lXbs2DFcffXVuOmmm/DKK68AAK6++mq8//776Nq1a6s3kIiA24dlYkiXdlF7dj+FRqhqntvHWE55iNhhMXHk+TQsZgGiKMDOL7OtymYW4TghDF87IHimrFmDM7SfvbICSygOy4SBf+TZWHXB0b7EeHOaLdtQFAVFRUVYtGgRAlNCS5KEr7/+WrvPVVdd1XotJCIA/uAU7TVxdHZCVfOcFGM95W0xVo48n04gNFtNYtSfMBjJLCbhVyPPJ+rSvnEk2icr2nSd0cYsipAMtnx1IDtHZ480T9fy3N988w2++eYbAP6peU68LEnS6X6diELAf6JSuFtB4SSrKkJxTtT5DXOFn0yMxcSp6k7DahJhaijb4F+p9dhM4hktGKKo0TtdpyFrnmHs9NxseO7atStHu4gigKKqUT8PKJ0dJURlG01ncjlRjNUUtVN+nQtWswhRFIKmSaPQ89c86w/DoihE78izgWueBYOm52bDc2Fh4TloBhE1R1EBjnW1bfI5+ALl4MjzadnM/r+PzcR3Y2uKs5lxeXaK7vuLiN7VVY1YO2/wkmf9i6QQUXipIZqmjKJXYGno1hTLmufTspn8U9RZzSJP4G1FJlFAZvuY5u/YQBSFqC3bMKLACYMG/F4AgOGZKGrIqjFHKEi/UJ0weDoju3c4o1rTtiYQmq1m0bjJIAqZhOgt2zCSi7LaAzD+yLOuEwaJKPwu7ZnMFQTbuKZLZ7cWIy5oEEo2kwgFaJh3PdytoQDWoEeG16b1B9B4wqBRe4ThmShKXH9B53A3gcLMX7YR7la0bTazCR5ZYc1zhLGfwcmF1Pq08x8N+ibhq42IKEqcixMG6fSsZqFxqjqOdEYMW5SuLmhUqsHrNvhqIyKKEorS+jXPdHrBU9WFuzUUYDOzTj+StPmp6oiIKDLIamjmeaaW69spHpKiwisrsDOwRQw7R54jirHHnRmeiYiihqyEZoVBajmzSYTZBM5IEmGivWzj8St6hrsJrcKoX/Wj+9VGRNSGKKrKOluik4j28Dy1/6lX/YxmRt1dRferjYioDYmxmuHyyeFuBlHEYQlNZDJodmZ4JiKKFu3sZlS7fOFuBlHE6dzOHu4mUBNT+qWGuwmtiuGZiChKTOmfhn5pCeFuBlHEuWZAeribQE386XJj1nAH8IRBIqIokZPO4ExEkU+rdTZo0TNHnomIiIgoZIQT/jcahmciIiIiCpnArEAGHXhmeCYiIiIi0ovhmYiIiIhCzqADzwzPRERERER6MTwTERERUcix5pmIiIiISDdjpmeGZyIiIiIinRieiYiIiCjkjDnuzPBMRERERKQbwzMRERERhRxPGCQiIiIi0smg2ZnhmYiIiIhCTzDo0DPDMxERERGRTgzPREREREQ6MTwTEREREenE8ExEREREIWfQkmeGZyIiIiIKPYNmZ4ZnIiIiIiK9zOFuQGvKz8/Hq6++CqvVql03ffp0XHzxxQCAZcuWYdmyZUhISMAdd9yBjIwMAEBZWRk++OADPPTQQxBFfr8gIiIiOlNGnarO0OEZAOLj4/HXv/71V9dXV1dj+fLlmDNnDrZs2YIFCxbgnnvuAQB8+umnmD59OoMzEREREQVps+mwsrISHTt2REJCArKzs1FeXg4A2LBhA5KTk5GVlRXeBhIRERFFMWOOO7eBkee6ujo8/PDDsFgsGDBgAKZMmQK73Y6UlBSUl5ejuroa+fn5SEtLg8vlwvLly/Hggw+26LmcTic8Hk+It+DUXC4XKisrz9nzUethXxoD+9E42JfGwb4Mjx5J9pD/7c91XyYlJZ30ekFVVfWcteIcq66uhtPpRGpqKiorK/HBBx+gU6dOuOmmmwAA69evx/Lly5GYmIjf/OY3WL58OXr16oW4uDgsXrwYoijiuuuu02qhI01lZeUpO5aiC/vSGNiPxsG+NA72ZXi88cMBxNlMuDW3a8geM1L60lAjzz///DM+/vhjAP5vC3PmzEFiYiIAIDk5Gddccw1ef/11LTzn5uYiNzcXAFBYWIiKigoMGjQIjz32GB5++GFUVVVh7ty5ePTRR8OzQURERERRimUbUWDo0KEYOnToKW8XBAEnG2hXFAWfffYZbr31VtTV1UFRFHTo0AEJCQk4fPhwazaZiIiIyHiMW9hgrPB8ovz8fCQnJyMpKQlVVVX46quvMGjQoF/db8WKFejfvz9SUlIgyzJ8Ph9KSkpQWVmJlJSUMLSciIiIKLoJBh17NnR4PnToEP7xj3/A6XQiLi4OAwcOxNSpU4Puc/z4cWzYsAEPP/wwAMBkMmHWrFl49dVXYbFYcPPNN4eh5URERETRy7jjzgY/YdDoIqVwns4e+9IY2I/Gwb40DvZleLy2aj/ax1hw04VdQvaYkdKXhh55JiIiIqJzb3i39nCYTeFuRqtgeCYiIiKikMrt2j7cTWg1bXaFQSIiIiKiM8XwTERERESkE8MzEREREZFODM9ERERERDoxPBMRERER6cTwTERERESkExdJISIiIiLSiSPPREREREQ6MTwTEREREenE8ExEREREpBPDMxERERGRTgzPREREREQ6MTwTEREREenE8ExEREREpBPDMxERERGRTgzPREREREQ6MTwTEREREenE8ExEREREpBPDcwTKy8vDl19+iSNHjoS7KRQCe/bswRtvvIFt27aFuyl0FoqKilBcXAy32w0AUBQlzC2ilqqoqIDL5dIuq6oaxtbQ2SgvL0d9fb12mX0ZnSRJAhA9+1VzuBtAjTweDz766CPs2rULkydPhs1mC3eT6CxUVlZi7ty52L9/P3w+H0aPHh3uJlEL1NTU4B//+AeOHj2KpKQk2O123HLLLYiPj4eqqhAEIdxNJJ2qq6vxwQcf4Pjx43A4HBg6dCiGDRsGu93OvowyNTU1+Nvf/obq6mrExMQgNzcXw4cPh8PhYF9GkePHj+OLL75AbGwsZs2aBVGMjjHd6GhlG7F//34oioKXXnoJl156KZKSksLdJDpDgVGPL774Ak8//TQ6deqE1157DQMGDMDu3buD7kORT5ZlfPrpp0hKSsILL7yA6667DmazGStWrAAAfkBHEUmS8PHHH6NTp0548skn0a9fP2zatAlffvlluJtGLbBgwQJ06NABzzzzDIYMGYK8vDx89tln4W4WnYHCwkK88847OHLkCIqKirBr1y4A0TH6zPAcQbZu3YrY2FhYLBb8+OOPmDt3LtasWYOysjIADF3RIBCm2rdvjyeffBIzZ84EAHTu3BmVlZXw+XwMXFGkpqYG1dXVyMnJAQBkZWUhNjYWHTt21O7D92V0KCsrg9PpxGWXXQZRFDF+/Hj069cPP/zwAw4cOABBEKLiQ7utUxQFLpcLFRUV6Nu3LwDg0ksvxfjx47F582bs3r2bfRklZFnGJZdcgptvvhnnnXceVq5cCQAQRTHi96sMz2Hi8XgA+F88AVarFUlJSfjkk0+wYsUKtGvXDuvXr8ebb74JgKNckSzQnz6fDwBw2WWXoUOHDlr/iqIIt9sNs9nMnXoEO/F9aTKZAADFxcWorq7GoUOHsHPnThQXF2PdunUA+L6MVCf2pdlsxqFDh2CxWAD4+zbw84IFCwAgag4ZtzXl5eWoqqoC4O8jn8+HmpoaxMbGatf16NEDl1xyiTb6zL6MPIF+DHwGdunSBRdccAG6du2Kvn37wu1248cffwQQ+YMSfHWdY/X19Xj33Xfx7LPPAvDvwAMvJIfDgc2bN6O6uhp/+tOfMHnyZDzwwAPw+XxYunQpgMh/QbU1J/Zn4MM4ILADz8nJwb59++B0OrlTj0Ane1/KsoyEhARMmTIFkiThtddew4svvoiRI0ciPj4eCxYswPfffw8gOg4zthWn6suUlBT06dMHf//731FYWAin04mCggJMnToVkiThwIEDYW45naiurg5vv/02Xn/9dbz33nv49NNPUVlZiYSEBHTu3PlXn4uXXHIJBEHAjh07wtlsOsGJ/fjZZ5/h+PHjsFqt2rldXbt2Re/evbF+/XrU1dVF/OdkZLfOYAInq3i9XrhcLu2NH/jgHTt2LGpqarB//34cP35c+73c3Fzs27cPAEe5Ismp+rPp0YRAf5nNZmRmZmL//v1haSud2qn6MfCB3KtXL4wdOxapqal44IEHMHHiRIwdOxYjRozA5s2boapqxO/o24rm+vK2226D1WrFggUL8NxzzyExMRE5OTmQZRnt27cPZ9PpBIcPH8b//u//wuFw4KmnnsJll12G6upq7dD+1KlTsXfvXuzYsUPbz9rtdsTHx8Pr9Yax5dRUc/0Y6LvY2Ficf/75cDgcUTEowdk2ziGTyYQLL7wQ3bp1w8GDB/HBBx/g0ksvhdVqhc/ng8ViwZQpU7Bo0SLs3bsXycnJAPyHOvr37x/m1tOJTtefiqIEBar4+HjU1NRoO4oTb6fw0dOPhw4dwp49ezBt2jTt98rKypCTk8MvtBHkdH0pSRLsdjvuuusuOJ1OKIqCpKQk1NTUQFEUHtWLMLW1tRg4cCDGjRsHQRAwZMgQ7Nu3T3u/JScnY9y4cfjkk0/wwAMPICUlBYmJiaivr0dCQkKYW08Bp+rHwPut6cwoaWlpGDJkCH744QcsWLAAmzZtwowZMyIy/zA8t6Li4mKUlJQgNTUVXbp0QWxsLAYNGgSLxYKUlBSsWLECc+fOxe23364FqYsvvhglJSVYvnw5du7cifLycvh8Plx55ZVh3ho6k/5sSlEUxMTEICUlBRs3bkT//v0ZnMPoTPoxsIPPzs5GfHw8Pv/8c/Ts2RNbtmyBy+XC+PHjw7w1bVtL9rEWiwUJCQkQRREHDhzAJ598gvPOOw/t2rUL78a0cSf2ZY8ePZCamgpBECDLMkwmEyRJ0mrZAf/o86FDh/D+++/jvPPOQ0FBAaxWa9AJvXRunWk/Nh18sFqtUFUVBQUFqKiowLRp0yIyOAMMz61CURR88cUX+PHHH9G3b1/k5eVh0qRJyM3NRWJiojaadd111+HFF1/EuHHj0LlzZ0iSBLPZjKlTp2LYsGHIz89H9+7dMWbMmHBvUpvW0v5UFAWCIEAURciyjJ49e6JTp07h3pw2q6X9GDgqdMcddyAvLw/79+9HTk4Oxo0bF+5NarPO5j0ZOOHsp59+wpIlSzBy5EhMmjQp3JvUZp2qLy+88EK0b98eiqJo5wYdOXJEe98F+vKWW25BUVERduzYgSFDhmDs2LFh3qK2qaX9GPhdQRCwdetWfPDBB5g0aRImTpwYxq1pHsNzK6ipqUFRURH+9Kc/IS0tDZs2bcIPP/wAr9eLiRMnatOwZGVlYfjw4fjnP/+Jxx57DGazGT6fD1arFV27dkXXrl3DvSmElvdn4EPaYrHAZDLhiiuu4IhzGLW0Hy0WC7xeL9LT05Gens6SmwhwNu9Jr9cLm82G888/HyNGjIDVag335rRpevoSANxuNzweDzIyMgD4T8aWJAkJCQno27evNm0dhUdL+xHwh2ez2YwuXbrgtddei4r3JD8BQqS+vl4rbi8sLERpaSnS0tKgKAouuOAC5OTk4NChQ9oSzYHDwbNmzUJ5eTk++eQTzJ49WzvBhcKrNfqTgevcC1U/Llu2THtM9mN4hPo92alTp6j4kDaiM+1LADhy5AhsNhuSkpKwbds2PProo1izZk24NoEQ+n5MTk6OmvckR57P0tGjRzF//nyIoqgdJuzcuTPsdjvy8/ORnZ0NABg8eDBKSkqwd+9e9OnTR3uBlJeXQxAE7Ny5EzfeeCNGjBgRzs1p89ifxsB+NA72pXG0pC979+4Nm82GvLw8yLKM119/HYcPH8a0adMwbNiwMG9R28R+5MjzWfnxxx/x6quvomvXrpg+fTq8Xi+++eYb1NfXIzs7G+vXr9fu265dO3Tu3BmlpaVQVRWqqqKqqgovvPACcnNz8fzzz3OnHmbsT2NgPxoH+9I4WtqXgH/6z5KSEhw+fBhZWVl46aWXojJwGQH70Y/h+SyUl5dj4sSJmDZtGlJTU3Hrrbdi27ZtiImJQe/evVFZWRn0QurZsyf27NkDt9sNQRDQvn17vPzyy5gxY0YYt4IC2J/GwH40DvalcbS0L10uF0wmEy666CK8+OKLuOqqq8K4FcR+9GPZxlkYNWoUzGb/n9Dn88FutyM5ORlerxfZ2dk4cuQIvvnmG6Snp6Nz5844dOgQ+vXrh7i4OO0xAqvrUPixP42B/Wgc7EvjaGlfBpbgjtQpy9oa9qMfw/NZCKxIpaoqLBaLdriwY8eOMJvNuPzyy1FRUYH3338fFosFZWVluPHGG2EymcLccjoZ9qcxsB+Ng31pHC3tS4vFEuaWU1PsRz+G5xAITPKdn5+vvYAAwOFw4JZbbkFlZSUOHjyIQYMGhbOZpBP70xjYj8bBvjQO9qUxtPV+ZHgOgcC8r4WFhdpck6tWrcLevXsxefJkdOrUCUlJSWFuJenF/jQG9qNxsC+Ng31pDG29HxmeQyCwgpzT6URdXR3++te/oqqqCjfccANXlItC7E9jYD8aB/vSONiXxtDW+5HhOURKS0uxa9cuHD58GFdccQWXCI1y7E9jYD8aB/vSONiXxtCW+1FQA8sw0VmRJAkrVqzA6NGjDVcY3xaxP42B/Wgc7EvjYF8aQ1vuR4ZnIiIiIiKduEgKEREREZFODM9ERERERDoxPBMRERER6cTwTERERESkE8MzEREREZFODM9ERERERDoxPBMRERER6cTwTERERESk0/8H0HapQQf0JG8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Daily Returns\n",
    "qs.plots.daily_returns(returns=vd[\"value_return\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drawdown\n",
    "qs.plots.drawdown(returns=vd[\"value_return\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drawdown Periods\n",
    "qs.plots.drawdowns_periods(returns=vd[\"value_return\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drawdown\n",
    "qs.plots.earnings(returns=vd[\"value_return\"], start_balance=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap\n",
    "qs.plots.monthly_heatmap(returns=vd[\"value_return\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative Returns\n",
    "qs.plots.returns(returns=vd[\"value_return\"], benchmark=vd[\"bm_return\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cumulative Returns\n",
    "# qs.plots.yearly_returns(returns=vd[\"value_return\"], benchmark=vd[\"bm_return\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rolling Sharpe\n",
    "qs.plots.rolling_sharpe(\n",
    "    returns=vd[\"value_return\"],\n",
    "    benchmark=vd[\"bm_return\"],\n",
    "    period=126,\n",
    "    period_label=\"6-Months\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rolling beta\n",
    "qs.plots.rolling_beta(\n",
    "    returns=vd[\"value_return\"],\n",
    "    benchmark=vd[\"bm_return\"],\n",
    "    window1=126,\n",
    "    window1_label=\"6-Months\",\n",
    "    window2=252,\n",
    "    window2_label=\"12-Months\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rolling volatility\n",
    "qs.plots.rolling_volatility(\n",
    "    returns=vd[\"value_return\"],\n",
    "    benchmark=vd[\"bm_return\"],\n",
    "    period=126,\n",
    "    period_label=\"6-Months\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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